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  • 1.
    Choudhury, Nanda
    et al.
    Indian Inst Management Bodh Gaya, India.
    Mukherjee, Rohan
    Int Management Inst Kolkata, India.
    Yadav, Rambalak
    Indian Inst Management Jammu, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Wang, Wei
    Univ Skovde, Sweden.
    Can machine learning approaches predict green purchase intention? -A study from Indian consumer perspective2024In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 456, article id 142218Article in journal (Refereed)
    Abstract [en]

    This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system.

  • 2.
    Lu, Fengyi
    et al.
    Xi An Jiao Tong Univ, Peoples R China.
    Zhou, Guanghui
    Xi An Jiao Tong Univ, Peoples R China.
    Zhang, Chao
    Xi An Jiao Tong Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Chang, Fengtian
    Changan Univ, Peoples R China.
    Lu, Qi
    Xian Univ Sci & Technol, Peoples R China.
    Xiao, Zhongdong
    Xi An Jiao Tong Univ, Peoples R China.
    Energy-efficient tool path generation and expansion optimisation for five-axis flank milling with meta-reinforcement learning2024In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145Article in journal (Refereed)
    Abstract [en]

    Five-axis flank milling is prevalent in complex surfaces manufacturing, and it typically consumes high electricity energy. To save energy and improve energy efficiency, this paper proposes a tool path optimisation of five-axis flank milling by meta-reinforcement learning. Firstly, considering flank milling features, a feed angle is defined that guides tool spatial motion and identifies an ideal principal path. Then, machining energy consumption and time are modelled by tool path variables, i.e., feed angle, cutting strip width and path length. Secondly, an energy-efficient tool path dynamic optimisation model is constructed, which is then described by multiple Markov Decision Processes (MDPs). Thirdly, meta-learning integrating with the Soft Actor-Critic (MSAC) framework is utilised to address the MDPs. In an MDP with one principal path randomly generated by a feed angle, cutting strip width is dynamically optimised under a maximum scallop height limit to realise energy-efficient multi-expansions. By quick traversal of MDPs with various feed angles, MSAC enables an energy-efficient path generation and expansion integrated scheme. Experiments show that, regarding machining energy consumption and time, the proposed method achieves a reduction of 69.96% and 68.44% over the end milling with an iso-scallop height, and of 41.50% and 39.80% over the flank milling with an iso-scallop height, with a minimum amount of machining carbon emission, which highlights its contribution to the arena of energy-oriented and sustainable intelligent manufacturing.

  • 3.
    Khan, Mohd Ziyauddin
    et al.
    Indian Inst Management Rohtak, India.
    Kumar, Ashwani
    Indian Inst Management Rohtak, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Gupta, Piyush
    Birla Global Univ, India.
    Sharma, Dheeraj
    Indian Inst Management Rohtak, India.
    Modeling enablers of agile and sustainable sourcing networks in a supply chain: A case of the plastic industry2024In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 435, article id 140522Article in journal (Refereed)
    Abstract [en]

    To compete in emerging markets, a supply chain must perform well. Agile and sustainable sourcing practices can improve supply chain performance; however, their impact needs an optimal evaluation. Although few research studies offer frameworks for integrating agile and sustainable principles, none offer links to implementing these practices in the sourcing networks of a supply chain. The present study seeks to bridge these gaps by developing a framework that identifies and configures the enabling elements for creating agile and sustainable sourcing networks. This study aims to provide an implementable causal model that the plastic industry's supply chain could adopt. In the first phase of the research process, fifteen enablers are identified through literature and validated by Delphi experts. In the second phase, interpretive structural modeling is applied to establish the hierarchical relationships among these enablers and categorize them based on their functionalities. The model is demonstrated based on the real-life case study of a firm manufacturing plastic pipes and fittings. The proposed model identifies the strategic, operational, and performance level enablers and intends to help the managers incorporate the agile and sustainable criteria in their sourcing practices. The findings of this study provide several contributions to the literature and implications for the plastic industry.

  • 4.
    Wang, Wenke
    et al.
    Sichuan Normal Univ, Peoples R China.
    Cao, Qilin
    Sichuan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Zhou, Cheng
    Sichuan Univ, Peoples R China.
    Jiao, Qinghui
    Sichuan Univ, Peoples R China.
    Mangla, Sachin Kumar
    OP Jindal Global Univ, India.
    Risk management of green supply chains for agricultural products based on social network evaluation framework2024In: Business Strategy and the Environment, ISSN 0964-4733, E-ISSN 1099-0836Article in journal (Refereed)
    Abstract [en]

    The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five-star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities.

  • 5.
    Ren, Shan
    et al.
    Xian Univ Posts & Telecommun, Peoples R China.
    Shi, Lichun
    Northwestern Polychn Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Cai, Weihua
    CRRC Tangshan Corp Ltd, Peoples R China.
    Zhang, Yingfeng
    Northwestern Polychn Univ, Peoples R China.
    A personalised operation and maintenance approach for complex products based on equipment portrait of product-service system2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 80, article id 102485Article in journal (Refereed)
    Abstract [en]

    Based on the holistic data of product-service system (PSS) delivery processes, equipment portrait can be used to describe personalised user requirements and conduct targeted analysis on the performance of complex products. Therefore, a promising application combining PSS and equipment portrait is to establish a more refined portrait model to improve the accuracy and applicability of operation and maintenance (OM) schemes for industrial products. However, studies in the above field are facing many challenges. For example, the research on equip-ment portrait in the industrial field is still in its infancy. PSS and equipment portrait are studied separately, and the overall solution that integrates PSS and equipment portrait for complex products OM service is almost vacant. A personalised OM approach for complex products (POMA-CP) is proposed to address these challenges. First, a framework of POMA-CP is developed to show how the processes of refined OM can be implemented. Then, a solution of POMA-CP based on the framework is designed. A multi-level case library, dynamic equipment portrait model, and case-pushing mechanism are established and developed. Active pushing of the best similar cases and automatic generation of service schemes are realised. Finally, an application scenario for a high-speed electric multiple units (EMU) bogie is presented to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy and applicability for service schemes are achieved, resulting in the efficient reusing of OM knowledge, proactive implementation of refined maintenance, and reducing maintenance cost and resource consumption.

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  • 6.
    Qin, Wei
    et al.
    Shanghai Jiao Tong Univ, Peoples R China.
    Zhuang, Zilong
    Shanghai Jiao Tong Univ, Peoples R China.
    Sun, Yanning
    Shanghai Jiao Tong Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Yang, Miying
    Cranfield Univ, England.
    An available-to-promise stochastic model for order promising based on dynamic resource reservation policy2023In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 61, no 16, p. 5525-5542Article in journal (Refereed)
    Abstract [en]

    Facing uncertain future customer orders, a pull-based available-to-promise (ATP) mechanism will deteriorate the overall profit since it allocates critical resources only to current customer orders. To prevent current less-profitable customer orders from over-consuming critical resources, this study investigates a push-pull based ATP problem with two time stages and three profit margin levels, and develops a dynamic resource reservation policy to maximise the expected total profit. Then, a corresponding push-pull based stochastic ATP model is established with known independent demand distributions, and the optimal reservation level is derived by the genetic algorithm to maximise the expected total profit. Finally, a series of simulation experiments are conducted to reveal the impact of some key factors, and the experiment results provide theoretical guidance and implementation methods for companies to maximise overall profits.

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  • 7.
    Ma, Shuaiyin
    et al.
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Huang, Yuming
    Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Liu, Haizhou
    Beijing Univ Posts & Telecommun, Peoples R China; China Gen Technol Grp Holding Co Ltd, Peoples R China.
    Chen, Yanping
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China.
    Wang, Jin
    Xian Univ Posts & Telecommun, Peoples R China.
    Xu, Jun
    Xidian Univ, Peoples R China.
    Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 349, article id 121608Article in journal (Refereed)
    Abstract [en]

    In Industry 4.0, the production data obtained from the Internet of Things has reached the magnitude of big data with the emergence of advanced information and communication technologies. The massive and low-value density of big data challenges traditional clustering and correlation analysis. To solve this problem, a big data-driven correlation analysis based on clustering is proposed to improve energy and resource utilisation efficiency in this paper. In detail, the production units with abnormal and energy-intensive consumption can be classified by using clustering analysis. Additionally, feature extraction is carried out based on clustering analysis and the same cluster data is migrated to the training data set to improve correlation analysis accuracy. Then, correlation analysis can balance the relationship between energy supply and demand, which can reduce carbon emission and enhance sustainable competitiveness. The sensitivity analysis results show that the feature extraction method can improve the correlation analysis accuracy compared to the original analysis model. In conclusion, the big data-driven correlation analysis based on clustering can uncover the potential relationship between energy consumption and product yield, thus improving the efficiency of energy and resources.

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  • 8.
    Cao, Cejun
    et al.
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Jiahui
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Wang, Haoheng
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Mengjie
    Chongqing Technol & Business Univ, Peoples R China.
    Digital twin-driven robust bi-level optimisation model for COVID-19 medical waste location-transport under circular economy2023In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 186, article id 109107Article in journal (Refereed)
    Abstract [en]

    COVID-19 medical waste collection-transport system, including the location of the related facilities, transport, and disposal, is a critical component in the circular economy. To promote the circularity of the waste management system and mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, environmental regulations, service level, and uncertainty in infection probability. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.

  • 9.
    Wang, Jin
    et al.
    Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Ren, Shan
    Xian Univ Posts & Telecommun, Peoples R China.
    Wang, Chuang
    Xian Univ Posts & Telecommun, Peoples R China.
    Ma, Shuaiyin
    Xian Univ Posts & Telecommun, Peoples R China.
    Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 79, article id 102435Article in journal (Refereed)
    Abstract [en]

    Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.

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  • 10.
    Ma, Shuaiyin
    et al.
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Huang, Yuming
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Kong, Xianguang
    Xidian Univ, Peoples R China.
    Yin, Lei
    Xidian Univ, Peoples R China.
    Chen, Gaige
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries2023In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 337, article id 120843Article in journal (Refereed)
    Abstract [en]

    Energy-intensive manufacturing industries are characterised by high pollution and heavy energy consumption, severely challenging the ecological environment. Fortunately, environmental, social, and governance (ESG) can promote energy-intensive manufacturing enterprises to achieve smart and sustainable production. In Industry 4.0, various advanced technologies are used to achieve smart manufacturing, but the sustainability of production is often ignored without considering ESG performance. This study proposes a strategy of edge-cloud cooperation -driven smart and sustainable production to realise data collection, preprocessing, storage and analysis. In detail, kernel principal component analysis (KPCA) is used to decrease the interference of abnormal data in the eval-uation results. Subsequently, an improved technique for order preference by similarity to ideal solution (TOPSIS) based on the adversarial interpretative structural model (AISM) is proposed to evaluate the production efficiency of the manufacturing workshop and make the analysis results more intuitive. Then, the architecture and models are verified using real production data from a partner company. Finally, sustainable analysis is discussed from the perspective of energy consumption, economic impact, greenhouse gas emissions and pollution prevention.

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  • 11.
    Lu, Fengyi
    et al.
    Xi An Jiao Tong Univ, Peoples R China.
    Zhou, Guanghui
    Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China.
    Zhang, Chao
    Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Chang, Fengtian
    Xi An Jiao Tong Univ, Peoples R China.
    Xiao, Zhongdong
    Xi An Jiao Tong Univ, Peoples R China.
    Energy-efficient multi-pass cutting parameters optimisation for aviation parts in flank milling with deep reinforcement learning2023In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 81, article id 102488Article in journal (Refereed)
    Abstract [en]

    Cutting parameters play a major role in improving the energy efficiency of the manufacturing industry. As the main processing method for aviation parts, flank milling usually adopts multi-pass constant and conservative cutting parameters to prevent workpiece deformation but degrades energy efficiency. To address the issue, this paper proposes a novel multi-pass parametric optimisation based on deep reinforcement learning (DRL), allowing parameters to vary to boost energy efficiency under the changing deformation limits in each pass. Firstly, it designs a variable workpiece deformation const.raint on the principle of stiffness decreasing along the passes, based on which it constructs an energy-efficient parametric optimisation model, giving suitable decisions that respond to the varying cutting conditions. Secondly, it transforms the model into a Markov Decision Process and Soft Actor Critic is applied as the DRL agent to cope with the dynamics in multi-pass machining. Among them, an artificial neural network-enabled surrogate model is applied to approximate the real-world machining, facilitating enough explorations of DRL. Experimental results show that, compared with the conventional method, the proposed method improves 45.71% of material removal rate and 32.27% of specific cutting energy while meeting deformation tolerance, which substantiates the benefits of the energy-efficient parametric opti-misation, significantly contributing to sustainable manufacturing.

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  • 12.
    Wang, Wenke
    et al.
    Sichuan Normal Univ, Peoples R China.
    Guo, Xinlin
    Sichuan Normal Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Tang, Aomei
    Sichuan Normal Univ, Peoples R China.
    Yang, Qin
    Sichuan Normal Univ, Peoples R China.
    Factors Affecting Unmanned Aerial Vehicles Unsafe Behaviors and Influence Mechanism Based on Social Network Theory2023In: Transportation Research Record, ISSN 0361-1981, E-ISSN 2169-4052, Vol. 2677, no 5, p. 1030-1045Article in journal (Refereed)
    Abstract [en]

    With the widespread application of unmanned aerial vehicles (UAVs), flight safety issues have gradually become prominent. To improve the safety level of UAV flight, a conceptual model was constructed through groups of unsafe behaviors of UAV flight based on the Swiss cheese model (reason model). The relationship network model of unsafe behaviors of UAV flight was built after using the two-mode and one-mode social network analysis, and the unsafe behaviors of UAV flight influence mechanism were studied by basic characteristics of network analysis, centrality analysis, core-periphery structure analysis, in/out-degree analysis, and structural hole analysis. The results showed that the two-mode network is closely related: unreasonable safety management structure of the organization and weak supervision of UAV flight operation were those unsafe behaviors of UAV supervision that had great influence. The unsafe behaviors of UAV supervision, such as the organizations illegal deployment of unqualified personnel for tasks and lack of ground commander for the mission plan, were in the core position of the network. The proposed model can effectively reduce the unsafe behaviors of UAV operations by eliminating critical unsafe behaviors of UAV supervision in the network and reducing UAV flight accidents.

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  • 13.
    Zhang, Ting
    et al.
    Shenzhen Technol Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Yang, Xintong
    Cardiff Univ, Wales.
    Chen, Jingjing
    Shenzhen Technol Univ, Peoples R China.
    Huang, Jiaming
    Shenzhen Technol Univ, Peoples R China.
    Home health care routing and scheduling in densely populated communities considering complex human behaviours2023In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 182, article id 109332Article in journal (Refereed)
    Abstract [en]

    This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as quality of life and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquain-tanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This studys theoretical and practical contributions are verified by feedback from researchers and practitioners.

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  • 14.
    Wen, Qianyun
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Lindfors, Axel
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    How should you heat your home in the green energy transition? A scenario-based multi-criteria decision-making approach2023In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 421, article id 138398Article in journal (Refereed)
    Abstract [en]

    The choice of heating system is significant for city planners and building owners alike, and many important areas, such as the well-being of residents, climate change impact and resource efficiency, may influence the choice. Understanding how to balance these areas is crucial for effective decision-making that can contribute to sustainable development and the green energy transition. However, these decisions represent complex problems where disparate knowledge areas must be considered simultaneously. When faced with this type of decisionmaking problem, employing different multi-criteria decision-making methods is common. However, such methods only provide a snapshot of which alternative is preferred and because of this, their results may become obsolete due to changes in the performance of alternatives or the value perceptions of the decision-makers. To overcome this challenge and to improve the longevity and reliability of multi-criteria decision-making results, the authors of this study explored a novel approach to producing semi-dynamic results through scenarios, which were used to consider possible future changes to the alternatives performance and the decision-makers value perceptions. The application of scenarios in the multi-criteria decision-making method enabled nuanced information to be produced on how the performance of different heating alternatives may change under different plausible futures. This approach was demonstrated by applying it to the case of residential heating in Denmark, where results showed that while final rankings varied across both scenarios and ranking methods, solar heating was the preferred alternative, while the oil boiler alternative performed the worst. Overall, this study highlights the importance of considering likely future changes to both the performance of alternatives and the value perceptions of decision-makers when making decisions with long lifetimes and suggests an approach for doing this.

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  • 15.
    Liu, Lingdi
    et al.
    Beihang Univ, Peoples R China.
    Song, Wenyan
    Beihang Univ, Peoples R China; Beihang Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies2023In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 178, article id 109113Article in journal (Refereed)
    Abstract [en]

    Facing the challenges of globalisation and unpredictable shocks, manufacturers seek novel methods to maintain the sustainability of their supply chains. Adopting Industry 4.0 (I4.0) technologies facilitates sustainable supply chain management (SSCM) with the precise decision-making of supply chain activities and the realisation of circular development. However, according to the bibliometric analysis and systematic literature review of ar-ticles related to "SSCM", few frameworks with I4.0 technologies are found to empower SSCM under circular economy (CE) logic. Thus, this article proposes a conceptual framework of I4.0 technologies-embedded SSCM, which takes advantage of five kinds of emerging digital technologies, including cloud services, artificial intel-ligence (AI), big data analytics (BDA), blockchain technology (BT), and internet of things (IoT). The CAB2IN framework is based on the technologies mentioned above alongside the design, manufacturing, delivering, using, and end-of-life stages of products and services to meet the requirements of reducing material usage, remanu-facturing, reusing, and recycling. This papers contribution lies in indicating the trends of SSCM in the era of Industry 4.0 and proposing CAB2IN to creatively establish the virtual side of circular SSCM, which leverages the data generated in each stage to assist sustainable decision-making. CAB2IN illuminates several research di-rections for future studies of digitalised SSCM under the perspective of CE. The case of Company S illustrates the application of CAB2IN in the healthcare supply chain. This paper also summarises insightful directions of digi-talised SSCM under the proposed circular framework.

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  • 16.
    Peng, Huatao
    et al.
    Wuhan Univ Technol, Peoples R China.
    Chang, Yuming
    Wuhan Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Risk preference, prior experience, and serial entrepreneurship performance: evidence from China2023In: Asia Pacific Business Review, ISSN 1360-2381, E-ISSN 1743-792X, Vol. 29, no 3, p. 613-631Article in journal (Refereed)
    Abstract [en]

    Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship.

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  • 17.
    Wang, Zhichao
    et al.
    Wuhan Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Lin, Zhenhong
    South China Univ Technol, Peoples R China.
    Hao, Han
    Tsinghua Univ, Peoples R China.
    Li, Shunxi
    Wuhan Univ Technol, Peoples R China; Univ Victoria, Canada; Wuhan Univ Technol, Peoples R China.
    Techno-economic comparison on charging modes of battery heavy-duty vehicles in short-haul delivery: A case study of China2023In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 425, article id 138920Article in journal (Refereed)
    Abstract [en]

    It is critical to arrange appropriate charging infrastructure in advance to decarbonise heavy freight through electrification. Based on the same service level, this study conducted a techno-economic comparison of charging modes for battery heavy-duty vehicles in short-haul delivery, covering the broadest range of charging modes, including slow, fast (150 kW), fast (350 kW), swap, and overhead catenary. The techno-economic performance is obtained by a model composed of five evaluation indicators, in which the ratio of service capacity to cost and the average financial net present value are specific. The factors influencing the techno-economic performance of charging modes are further explored based on case analysis. Results show that the charging modes of slow, fast (150 kW), fast (350 kW), swap, and overhead catenary are not profitable under the corresponding facility utilisation rates of 40%, 20%, 20%, 30%, and 70%, or under operating years of 5, 3, 2, 4, and 12 years. Fast charging, at both 150 and 350 kW, has a better advantage in profitability based on the highest average financial net present value. Swap charging is best regarding energy supplement efficiency, but it is not profitable when the battery swapping price is less than 0.8 CNY/kWh. Overhead catenary charging is the most effective system per unit cost due to the highest ratio of service capacity to cost. The insights, the precise prediction of the charging demand, the focus on the charging price, and the comprehensive improvement in the facility utilisation rate are crucial for the success of charging service providers.

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  • 18.
    Sassanelli, Claudio
    et al.
    Politecn Bari, Italy; Ecole Ponts ParisTech ENPC, France.
    Garza-Reyes, Jose Arturo
    Univ Derby, England.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Pacheco, Diego Augusto de Jesus
    Aarhus Univ, Denmark.
    Luthra, Sunil
    All India Council Tech Educ AICTE, India.
    The disruptive action of Industry 4.0 technologies cross-fertilizing Circular Economy throughout society2023In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550, Vol. 183, article id 109548Article in journal (Refereed)
    Abstract [en]

    The Industry 4.0 concept, invariably more intertwined with the Circular Economy paradigm, has been increasingly applied outside the manufacturing domain. As a side-effect, Industry 4.0 technologies are disruptively reshaping, under the threefold Triple Bottom Line perspective, supply chains and different contexts such as innovation management and societal development. The eleven papers published in this special issue aim at contributing to the emerging research stream about the sustainable development of I4.0-CE-driven supply chains. In particular, four main macro-topics emerged: (i) I4.0-driven Circular Supply Chains (CSCs); (ii) Life Cycle Information in CE ecosystems; (iii) Waste management process optimisation; (iv) Drivers, challenges, opportunities, and barriers for I4.0-CE-driven supply chains. In the studies included in this special issue collection, novel conceptual frameworks and methodologies are proposed, and some practical cases are also conducted, promoting relevant advances in the twofold field of I4.0-CE, triggering the green and digital twin transition towards a supply chain and social dimension. Nevertheless, the analysis of the papers constituting the special issue reveals promising research opportunities necessary to investigate and explore this emerging domain in its specific facets. More quantitative studies are needed to understand better the dynamics in CSC, CE ecosystems and Industrial Symbiosis networks. Finally, developing specialised circular and sustainable technologies based on intelligent architectures and adopting data-driven approaches could support the adoption of circular practices not only within the companies but also outside them in the CSC and social dimensions.

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  • 19.
    Cao, Cejun
    et al.
    Chongqing Technol & Business Univ, Peoples R China; Chongqing Technol & Business Univ, Peoples R China.
    Xie, Yuting
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Liu, Jiahui
    Chongqing Technol & Business Univ, Peoples R China.
    Zhang, Fanshun
    Xiangtan Univ, Peoples R China.
    Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability2023In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 389, article id 135985Article in journal (Refereed)
    Abstract [en]

    A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.

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  • 20.
    Chen, Jian
    et al.
    Nanjing Univ Aeronaut & Astronaut, Peoples R China.
    Ning, Tong
    Nanjing Univ Aeronaut & Astronaut, Peoples R China.
    Xu, Gangyan
    Hong Kong Polytech Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    A memetic algorithm for energy-efficient scheduling of integrated production and shipping2022In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 35, no 10-11, p. 1246-1268Article in journal (Refereed)
    Abstract [en]

    Energy-efficient manufacturing is critical as the industrial sector accounts for a substantial portion of global energy consumption. This research aims to address an energy-efficient scheduling problem of production and shipping for minimizing both makespan and energy consumption. It contributes to an integrated energy-efficient production and shipping system, which is separately studied in most existing research. The production stage allocates jobs onto unrelated parallel machines that can be shut off and adjust their cutting speed to save energy. The shipping stage aims to allocate jobs to vehicles of various sizes with varied unit energy consumption. The problem is modelled as a mixed-integer quadratic program. Considering its complexity, a memetic algorithm (MA) is proposed to incorporate a knowledge-driven local search strategy considering the balance between exploration and exploitation. Two dominance rules are derived from the characteristics of the specific problem and embedded into the proposed MA to enhance its performance. Experimental results demonstrate that the proposed MA outperforms two other population-based algorithms, genetic algorithm and traditional MA, in terms of performance and computing time. This research practically contributes to improving productivity and energy efficiency for the production-shipping supply chain of make-to-order products.

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  • 21.
    Wang, Wenke
    et al.
    Sichuan Normal Univ, Peoples R China.
    Li, Kang
    Sichuan Normal Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Lian, Jiayao
    Sichuan Normal Univ, Peoples R China.
    Hong, Shu
    Sichuan Normal Univ, Peoples R China.
    A system dynamics model analysis for policy impacts on green agriculture development: A case of the Sichuan Tibetan Area2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 371, article id 133562Article in journal (Refereed)
    Abstract [en]

    Tibetan area of Sichuan Province is the green shelter for national ecological security, so the local agriculture should follow the path of green development rather than the traditional way. To develop green agriculture, proper policies need to be put forward. Therefore, it is necessary to simulate and test the policies proposed and see the long-term outcomes of implementing each policy before making the final decisions. The system dynamics model is a suitable way to simulate the impact of various policies on the development of green agriculture in the Sichuan Tibetan area from a dynamic and systematic perspective. Unlike existing studies that only consider unilateral policy impact, this paper conducts policy simulations from multiple aspects of population, investment and ecological environment. The simulation results showed that population policy and investment policy had a significant effect on improving the agricultural economic benefits. The agricultural output value will increase maximum of 2.8% if the population grows by 1.2% and more rural people are engaged in agricultural activities. The grain output will increase maximumly 9.8% if the sown area of crops increases by 1.2% each year and the investment in agricultural fixed assets increases by 2%. Green policy played a significant role in improving the ecological benefits of agriculture, and the maximum relative ratio of the green index of agricultural development was 12.2%. Finally, some suggestions are put forward, including government investment, protection of the agricultural ecological environment, and promotion of the agricultural market. It is anticipated that this system dynamic model and suggestions can contribute to the green development of agriculture in the Sichuan Tibetan area and other areas.

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  • 22.
    Ren, Shan
    et al.
    Xian Univ Posts & Telecommun, Peoples R China; Northwestern Polytech Univ, Peoples R China.
    Zhang, Yingfeng
    Northwestern Polytech Univ, Peoples R China; Northwestern Polytech Univ, Peoples R China.
    Sakao, Tomohiko
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Cai, Ruilong
    Northwestern Polytech Univ, Peoples R China; Beijing Jingdiao Grp Co Ltd, Peoples R China.
    An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning2022In: International Journal of Precision Engineering and Manufacturing-Green Technology, ISSN 2288-6206, E-ISSN 2198-0810, Vol. 9, no 1, p. 287-303Article in journal (Refereed)
    Abstract [en]

    As a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the providers saving of the maintenance and operation cost.

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  • 23.
    Zhang, Xizhao
    et al.
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China.
    Hao, Xu
    Univ Sci & Technol Beijing, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Wu, Rui
    Minist Transport, Peoples R China.
    Shan, Xiaonian
    Hohai Univ, Peoples R China.
    Li, Shunxi
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China; Univ Victoria, Canada.
    Contribution of potential clean trucks in carbon peak pathway of road freight based on scenario analysis: A case study of China2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 379, article id 134669Article in journal (Refereed)
    Abstract [en]

    Reducing the carbon emissions from trucks is critical to achieving the carbon peak of road freight. Based on the prediction of truck population and well-to-wheel (WTW) emission analysis of traditional diesel trucks and po-tential clean trucks including natural gas, battery-electric, plug-in hybrid electric, and hydrogen fuel cell, the paper analyzed the total greenhouse gas (GHG) emissions of Chinas road freight under four scenarios, including baseline, policy facilitation (PF), technology breakthrough (TB), and PF-TB. The truck population from 2021 to 2035 is predicted based on regression analysis by selecting the data from 2002 to 2020 of the main variables, such as the GDP scale, road freight turnover, road freight volume, and the number of trucks. The study forecasts the truck population of different segments, such as mini-duty trucks (MiDT), light-duty trucks (LDT), medium-duty trucks (MDT), and heavy-duty trucks (HDT). Relevant WTW emissions data are collected and adopted based on the popular truck in Chinas market, PHEVs have better emission intensity, especially in the HDT field, which reduces by 51% compared with ICEVs. Results show that the scenario of TB and PF-TB can reach the carbon peak with 0.13% and 1.5% total GHG emissions reduction per year. In contrast, the baseline and PF scenario fail the carbon peak due to only focusing on the number of clean trucks while lacking the restrictions on the GHG emission factors of energy and ignoring the improvement of trucks energy efficiency, and the total emissions increased by 29.76% and 16.69% respectively compared with 2020. As the insights, adopting clean trucks has an important but limited effect, which should coordinate with the transition to low carbon energy, and the melioration of clean trucks to reach the carbon peak of road freight in China.

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  • 24.
    Ma, Shuaiyin
    et al.
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China; Shaanxi Union Res Ctr Univ, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Ding, Wei
    Xian Univ Posts & Telecommun, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China; Xian Key Lab Big Data & Intelligent Comp, Peoples R China; Shaanxi Union Res Ctr Univ, Peoples R China; Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Ren, Shan
    Xian Univ Posts & Telecommun, Peoples R China.
    Yang, Haidong
    Guangdong Univ Technol, Peoples R China.
    Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 326, article id 119986Article in journal (Refereed)
    Abstract [en]

    Internet of Things (IoT) technology, which has made manufacturing processes more smart, efficient and sustainable, has received increasing attention from the industry and academia. As one of the most important applications for IoT, sustainable smart manufacturing enables lower cost, higher productivity and flexibility, better quality and sustainability during the product lifecycle management. Over the years, numerous enterprises have promoted the implementation of both sustainable and smart manufacturing. In the Industry 4.0 context, a digital twin is widely used to achieve smart manufacturing, although this approach often ignores sustainability. This study aims to simultaneously consider digital twin and big data technologies to propose a sustainable smart manufacturing strategy based on information management systems for energy-intensive industries (EIIs) from the product lifecycle perspective. The integration of digital twin and big data provides key technologies for data acquisition in energy-intensive production environments, prediction and mining in uncertain environments as well as real-time control in complex working conditions. Moreover, a digital twin-driven operation mechanism and an overall framework of big data cleansing and integration are designed to explain and illustrate sustainable smart manufacturing. Two case studies from Southern and Northern China demonstrate the efficacy of the strategy, with the results showing that Companies A and B achieved the goals of energy saving and cost reduction after implementing the proposed strategy. By applying an energy management system, the unit energy consumption and energy cost of production in Company A decreased by at least 3%. In addition, the cradle-to-gate lifecycle big data analysis indicates that the costs of environmental protection in Company B decrease significantly. Finally, the effectiveness of the proposed strategy and some managerial insights for EIIs in China are analysed and discussed.

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  • 25.
    Kücükgül, Egemen
    et al.
    Linköping University, Department of Management and Engineering. Linköping University, Faculty of Science & Engineering.
    Cerin, Pontus
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Enhancing the value of corporate sustainability: An approach for aligning multiple SDGs guides on reporting2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 333, article id 130005Article in journal (Refereed)
    Abstract [en]

    In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

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  • 26.
    Lu, Fengyi
    et al.
    Xi An Jiao Tong Univ, Peoples R China.
    Zhou, Guanghui
    Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Zhang, Chao
    Xi An Jiao Tong Univ, Peoples R China; Xi An Jiao Tong Univ, Peoples R China.
    Ensemble transfer learning for cutting energy consumption prediction of aviation parts towards green manufacturing2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 331, article id 129920Article in journal (Refereed)
    Abstract [en]

    Cutting energy consumption prediction gives decision supports for the energy-saving operation to realise green manufacturing. However, there are challenges when predicting aviation parts due to the machining features causing tool wear and expensive data labelling. Consequently, this paper builds a prediction model and emphasises training with limited experimental data by proposing an ensemble transfer learning approach. The approach incorporates transfer learning, i.e., TrAdaBoost-R2 (TR) algorithm, and calibration, i.e., Bayesian and Markov chain Monte Carlo calibration (MCMC). Firstly, a cutting energy consumption prediction model considering tool wear is formulated with cutting and tool parameters as the inputs. Secondly, a dataset including experiment and simulation data for training is constructed, where TR is used to identify the valuable data from the simulation model calibrated by MCMC. Then random forest regression (RFR) is introduced as a base learner to train the prediction model on the hybrid dataset. Finally, a case study of the aluminium alloy 7075 parts milling process shows that the proposed method is accurate in cutting energy consumption prediction. Compared with RFR and TR-RFR, the proposed methods coefficient of determination (R2) increases by 11.60% and 3.55%, indicating high goodness of fit under the same small samples of the experiment. Therefore, the proposed method could help determine the most efficient process plan without excessive time, materials and energy, significantly contributing to green manufacturing.

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  • 27.
    Peng, Huatao
    et al.
    Wuhan Univ Technol, Peoples R China.
    Li, Bingbing
    Wuhan Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    How Social Network Influences the Growth of Entrepreneurial Enterprises: Perspective on Organizational and Personal Network2022In: SAGE Open, E-ISSN 2158-2440, Vol. 12, no 2, article id 21582440221108178Article in journal (Refereed)
    Abstract [en]

    Network size, network density, and tie strength together determine the function of social network and affect the growth of entrepreneurial enterprises. However, how the role of network size, network density, and tie strength on the growth of entrepreneurial enterprises remains inconsistent, as well as the effect of organizational and personal network remains unclear. To solve these relationships, we employ meta-analysis to reach study goals by researching 31 independent samples from 28 references with 5,259 observations. Results have shown two main findings: (1) Both network size and tie strength have a positive and significant impact on the growth of entrepreneurial enterprises, while network density does not correlate with the growth. (2) Organizational network mainly plays a positive effect between network size and growth, while personal network plays a more significant role in the relationship of tie strength and growth than organizational network. These results promote managers to take productive strategies for entrepreneurial enterprises growth. Our study provides a meta-analysis to merge different sounds about the relationship of network properties to the growth of entrepreneurial enterprises, emphasizing moderators of organizational and personal networks among these above relationships. Thus, these findings make significant contributions to the field of entrepreneurship.

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  • 28.
    Hasan, Md. Bokhtiar
    et al.
    Islamic Univ, Bangladesh.
    Ali, Md. Sumon
    Islamic Univ, Bangladesh.
    Uddin, Gazi Salah
    Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences.
    Al Mahi, Masnun
    Univ Liberal Arts Bangladesh ULAB, Bangladesh.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Park, Donghyun
    Asian Dev Bank, Philippines.
    Is Bangladesh on the right path toward sustainable development? An empirical exploration of energy sources, economic growth, and CO2 discharges nexus2022In: Resources policy, ISSN 0301-4207, E-ISSN 1873-7641, Vol. 79, article id 103125Article in journal (Refereed)
    Abstract [en]

    The sustainability of the recent economic progress of Bangladesh is critically dependent on how it faces envi-ronmental challenges, as the country is one of the primary victims of climate alteration. Taking into account the crucial roles of energy sources in this scenario, we analyze the impacts of non-renewable and renewable energy consumption (NREC and REC) on the growth-environment nexus in Bangladesh from 1980 to 2018. Based on the Auto-Regressive Distributed Lag (ARDL) model with and without structural breaks and policy dummies, our findings show that REC significantly upsurges economic growth, whereas NREC diminishes it. However, NREC leads to environmental deterioration, while REC enhances environmental quality. Besides, our results fail to support the Environmental Kuznets Curve hypothesis for Bangladesh. Interestingly, the policy dummy upsurges CO2 discharges while lessening economic growth, implying that the Bangladesh governments policies do not adequately cut pollution. Our Toda-Yamamoto non-causality test indicates a unidirectional causality running from GDP and its square term and NREC to CO2 emissions. Our findings suggest that policymakers in Bangladesh should adopt and implement strategies like enhancing renewable energy production, investment subsidies, tax credits, quota policies, and technological advancements to boost REC while plunging NREC to achieve economic sustainability.

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  • 29.
    Tian, Tingting
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Hong Kong Polytech Univ, Peoples R China; Tongji Univ, Peoples R China.
    Liu, Guangfu
    Tongji Univ, Peoples R China.
    Yasemi, Hussein
    Linköping University, Department of Management and Engineering. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Managing e-waste from a closed-loop lifecycle perspective: Chinas challenges and fund policy redesign2022In: Environmental Science and Pollution Research, ISSN 0944-1344, E-ISSN 1614-7499, Vol. 29, p. 47713-47724Article in journal (Refereed)
    Abstract [en]

    E-waste is one of the fastest growing streams of solid waste globally, and its effective management has become a focused issue, which requires a deep understanding of the core guiding theory of extended producer responsibility (EPR). Over the past 20 years, China, one of the worlds largest producers of electrical and electronic equipment (EEE), has made great efforts to improve e-waste management along with the massive generation of e-waste. In 2012, China implemented a unique EPR-based e-waste fund policy. However, the fund policy is unsustainable due to the challenges of non-closed resource use, informal recycling, and fund imbalance. Beginning with an overview of these challenges, this paper focuses on redesigning the fund policy from a closed-loop lifecycle perspective in order to maintain a balanced development of the resource use loop and the fund system in Chinas ten-year plan. In doing so, two EPR instruments, recycling content standards and consumer-oriented deposits, are added to the current fund policy. Subsequently, three extension scenarios alternately changed a critical parameter of the model to test the impact on sustainable capabilities. In this way, the sustainable supply of funds and secondary resources for the e-waste industry can be established in China and effectively demonstrate solid waste management in developing countries.

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  • 30.
    Mojumder, Abhishek
    et al.
    Indian Inst Management Rohtak, India.
    Singh, Amol
    Indian Inst Management Rohtak, India.
    Kumar, Ashwani
    Indian Inst Management Rohtak, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Mitigating the barriers to green procurement adoption: An exploratory study of the Indian construction industry2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 372, article id 133505Article in journal (Refereed)
    Abstract [en]

    The construction sector in India is in the governments intense focus on creating a world-class infrastructure balancing environmental conservation. Adopting green procurement practices by mitigating adoption barriers is necessary for Indian construction firms to achieve the goal. However, none of the existing research identified the comprehensive list of barriers, analysed their impacts on green procurement adoption, prioritized the barriers and formulated the solution strategies to mitigate them and maximize green procurement adoption for the Indian construction sector. To bridge this gap, this study has identified barriers, analysed their impact, prioritized the criticality, and developed the solution strategies to alleviate them. Questionnaire surveys and descriptive sta-tistics are first performed for data analysis of the firms based on the firms size and domain of expertise. Later, an analysis of variance (ANOVA) is performed and identified the significant differences in the impact of the barriers on Indian construction firms having different sizes or domains of expertise. The fuzzy best-worst method (FBWM) is then used to identify the most significant barriers as "reduced commitment from higher management ", "lack of management support ", and "perception of higher cost for adhering to green procurement ". Finally, the Delphi technique and assessment of various portals of the Government of India (GOI) have been carried out to identify the solutions to mitigate the barriers. The research results in an original and unique approach to identifying and analysing the critical barriers to green procurement adoption and their impact on different categories of Indian construction firms. It has identified the topmost barriers and then the solution strategies Indian construction firms and GOI need to focus on to embrace green procurement. The procurement managers can identify the top -rated barriers derived from the present study to closely focus and make strategies to eliminate them, helping their organisations adopt green procurement practices. The solution strategies derived from the study may be ready to implement action plans for construction and infrastructure companies of India, environmental and social development of the country, and assisting the GOI in developing and implementing the policy of green pro-curement for the Indian construction industry.

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  • 31.
    Liu, Yang
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Yang, Miying
    Cranfield Univ, England.
    Guo, Zhengang
    Imperial Coll London, England.
    Reinforcement learning based optimal decision making towards product lifecycle sustainability2022In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 35, no 10-11, p. 1269-1206Article in journal (Refereed)
    Abstract [en]

    Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact.

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  • 32.
    Bao, Xing
    et al.
    Zhejiang Univ Finance & Econ, Peoples R China.
    Wei, Wei
    Zhejiang Univ Finance & Econ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Oulu, Finland.
    Remanufacturing lead time planning of the medical device with multi-refurbishing steps2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 379, article id 134697Article in journal (Refereed)
    Abstract [en]

    This research is motivated by the challenges a ventilator remanufacturer encountered during the COVID-19 pandemic: (i) three refurbishing steps, namely, disassembling, sterilising, and reconditioning, which reduce the yield rates of reused components and thus complicating the remanufacturing process, are required to satisfy the compulsory hygienic regulations; and (ii) the lead time to procuring new components become rather variable because of the paralysed global logistics, thereby prolonging the remanufacturing time. To minimise the total remanufacturing costs, mathematical models are built to derive the optimal remanufacturing lead time analyt-ically for one-and two-component cases and numerical studies are conducted to investigate the behaviour of the remanufacturing process. Four managerial insights are provided to improve the remanufacturing performance: (i) The minimum relative entropy method could approximate the optimal remanufacturing lead time with higher precision because the remanufacturing time might be multi-modal distributed. (ii) Increasing the yield rates at all refurbishing steps could shorten the remanufacturing lead time but does not lower the total cost necessarily. (iii) Investment in reducing the refurbishing lead times might not be economically efficient, whereas shortening the procurement lead time could lower the cost dramatically. (iv) Stock-based strategy for the components with low holding cost could help simplify the remanufacturing process and save the multi-skilled labour cost.

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  • 33.
    Qin, Wei
    et al.
    Shanghai Jiao Tong Univ, Peoples R China.
    Zhuang, Zilong
    Shanghai Jiao Tong Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Xu, Jie
    Shanghai Jiao Tong Univ, Peoples R China.
    Sustainable service oriented equipment maintenance management of steel enterprises using a two-stage optimization approach2022In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 75, article id 102311Article in journal (Refereed)
    Abstract [en]

    Equipment maintenance management is essential for steel enterprises to provide sustainable production services. However, few studies have considered the equipment maintenance scheduling of steel enterprises due to the characteristics of large-scale and complex constraints. This study analyses the characteristics of equipment maintenance scheduling in the steel industry and incorporates multiple complex constraints into a mathematical model. To find an optimal or near optimal solution, a two-stage optimization approach is developed that introduces the rule-based prescheduling method to quickly obtain a reasonable maintenance plan in the first stage and employs a modified genetic algorithm to further optimize the prescheduling maintenance plan in the second stage. The case of Baowu Steel is used for verification and the experimental results demonstrate the superiority and effectiveness of the proposed optimization approach, which improves the comprehensive performance by 40.3% on the basis of prescheduling.

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  • 34.
    Kumar, Ashwani
    et al.
    Indian Inst Management Rohtak, India.
    Gaur, Diptanshu
    Indian Inst Management Rohtak, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Sharma, Dheeraj
    Indian Inst Management Rohtak, India.
    Sustainable waste electrical and electronic equipment management guide in emerging economies context: A structural model approach2022In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 336, article id 130391Article in journal (Refereed)
    Abstract [en]

    With globalization and the rapid advancement of information technology, waste electrical and electronic equipment (WEEE) management has become a significant concern among electronic manufacturers. It motivated researchers to identify barriers and enablers of sustainable WEEE management. However, existing literature could not capture multi-stakeholders perspective while identifying enablers crucial for developing sustainable WEEE management policy, especially in emerging economies. The present study fulfils the gap by considering multi-stakeholders perspective to identify enablers of sustainable WEEE management in an emerging economy, i.e., India. We identified 23 potential enablers through literature review and discussion with domain experts. Subsequently, the finalized enablers were analyzed to uncover the cause-effect relationship using a hybrid grey based decision-making trial and evaluation laboratory (DEMATEL) approach. Findings revealed that research and development capabilities and digitization, extended producer responsibility, monitoring of illegal import and dumping, environmental regulations and WEEE policies, and use of green or cleaner technologies for waste recycling were recognized as the most significant causal enablers. The study contributes to the theoretical knowledge by categorizing enablers under different theoretical frameworks. It can also assist policymakers, practitioners, and electronic manufacturers in framing policies related to the circular economy and sustainable WEEE management to meet the sustainable development goals of 2030.

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  • 35.
    Cao, Cejun
    et al.
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Tang, Ou
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Gao, Xuehong
    Univ Sci & Technol Beijing, Peoples R China.
    A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains2021In: International Journal of Production Economics, ISSN 0925-5273, E-ISSN 1873-7579, Vol. 235, article id 108081Article in journal (Refereed)
    Abstract [en]

    In the aftermath of large-scale natural disasters, supply shortage and inequitable distribution cause various losses, hindering humanitarian supply chains? performance. The optimal decisions are difficult due to the complexity arising from the multi-period post-disaster consideration, uncertainty of supplies, hierarchal decision levels and conflicting objectives in sustainable humanitarian supply chains (SHSCs). This paper formulates the problem as a fuzzy tri-objective bi-level integer programming model to minimize the unmet demand rate, potential environmental risks, emergency costs on the upper level of decision hierarchy and maximize survivors? perceived satisfaction on the lower level of decision hierarchy. A hybrid global criterion method is devised to incorporate a primal-dual algorithm, expected value and branch-and-bound approach in solving the model. A case study using data from the Wenchuan earthquake is presented to evaluate the proposed model. Study results indicate that the hybrid global criterion method guides an optimal strategy for such a complex problem within a reasonable computational time. More attention should be attached to the environmental and economic sustainability aspects in SHSCs after golden rescue stage. The proposed bi-level optimization model has the advantages of reducing the total unmet demand rate, total potential environmental risks and total emergency costs. If the decision-agents with higher authorities act as the leaders with dominant power in SHSCs, the optimal decisions, respectively taking hierarchical and horizontal relationships into account would result in equal performance.

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  • 36.
    Liu, Quan
    et al.
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China.
    Liu, Zhihao
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China; KTH Royal Inst Technol, Sweden.
    Xiong, Bo
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China.
    Xu, Wenjun
    Wuhan Univ Technol, Peoples R China; Wuhan Univ Technol, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function2021In: Advanced Engineering Informatics, ISSN 1474-0346, E-ISSN 1873-5320, Vol. 49, article id 101360Article in journal (Refereed)
    Abstract [en]

    Aiming at human-robot collaboration in manufacturing, the operators safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.

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  • 37.
    Wang, Jin
    et al.
    Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Ren, Shan
    Xian Univ Posts & Telecommun, Peoples R China.
    Wang, Chuang
    Xian Univ Posts & Telecommun, Peoples R China.
    Wang, Wenbo
    Jiangsu Univ, Peoples R China.
    Evolutionary game based real-time scheduling for energy-efficient distributed and flexible job shop2021In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 293, article id 126093Article in journal (Refereed)
    Abstract [en]

    With the global energy crisis and environmental issues becoming severe, more attention has been paid to production scheduling considering energy consumption than ever before. However, in the context of intelligent manufacturing, most studies apply the industrial internet of things (IIoT) to improve energy efficiency. It may cause the real-time data in the workshop unable to be collected and treated timely, thus affecting the real-time decision-making of the scheduling system. Edge computing (EC) can make full use of embedded computing capabilities of field devices to process real-time data and reduce the response time of making production decisions. Therefore, in this study, an overall architecture of the EC-IIoT based distributed and flexible job shop real-time scheduling (DFJS-RS) is proposed to enhance the real-time decision-making capability of the scheduling system. The DFJS-RS method, which consists of the task assignment method of the shop floor layer and the RS method of the flexible manufacturing units (FMUs) layer, is designed and developed. An evolutionary game-based solver method is adopted to obtain the optimal allocation. Finally, a case study is employed to validate the DFJS-RS method. The results show that compared with the existing production scheduling method, the DFJS-RS method can improve energy efficiency by up to 26%. This improvement can further promote cleaner production (CP) and sustainable societal development. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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  • 38.
    Wen, Qianyun
    et al.
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Yan, Qiyao
    Dalian Maritime Univ, Peoples R China.
    Qu, Junjie
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Fuzzy Ensemble of Multi-Criteria Decision Making Methods for Heating Energy Transition in Danish Households2021In: Mathematics, E-ISSN 2227-7390, Vol. 9, no 19, article id 2420Article in journal (Refereed)
    Abstract [en]

    More than 110 countries, including 500 cities worldwide, have set the goal of reaching carbon neutrality. Heating contributes to most of the residential energy consumption and carbon emissions. The green energy transition of fossil-based heating systems is needed to reach the emission goals. However, heating systems vary in energy source, heating technology, equipment location, and these complexities make it challenging for households to compare heating systems and make decisions. Hence, a decision support tool that provides a generalized ranking of individual heating alternatives is proposed for households as decision makers to identify the optimal choice. This paper presents an analysis of 13 heating alternatives and 19 quantitative criteria in technological, environmental, and financial aspects, combines ideal solution-based multi-criteria decision making with 6 weighting methods and 4 normalization methods, and introduces ensemble learning with a fuzzy membership function derived from Cauchy distribution to finalize the ultimate ranking. The robustness of the proposed method is verified by three sensitive analyses from different aspects. Air-to-water heat pump, solar heating and direct district heating are the top three rankings in the final result under Danish national average data. A framework is designed to guide decision makers to apply this ranking guideline with their practical, feasible situations.

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  • 39.
    Peng, Chen
    et al.
    Zhejiang Univ, Peoples R China.
    Peng, Tao
    Zhejiang Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Geissdoerfer, Martin
    Univ Cambridge, England.
    Evans, Steve
    Univ Cambridge, England.
    Tang, Renzhong
    Zhejiang Univ, Peoples R China.
    Industrial Internet of Things enabled supply-side energy modelling for refined energy management in aluminium extrusions manufacturing2021In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 301, article id 126882Article in journal (Refereed)
    Abstract [en]

    To improve industrial sustainability performance in manufacturing, energy management and optimi-sation are key levers. This is particularly true for aluminium extrusions manufacturing dan energy -intensive production system with considerable environmental impacts. Many energy management and optimisation approaches have been studied to relieve such negative impact. However, the effectiveness of these approaches is compromised without the support of refined supply-side energy consumption information. Industrial internet of things provides opportunities to acquire refined energy consumption information in its data-rich environment but also poses a range of difficulties in implementation. The existing sensors cannot directly obtain the energy consumption at the granularity of a specific job. To acquire that refined energy consumption information, a supply-side energy modelling method based on existing industrial internet of things devices for energy-intensive production systems is proposed in this paper. First, the job-specified production event concept is proposed, and the layout of the data acqui-sition network is designed to obtain the event elements. Second, the mathematical models are developed to calculate the energy consumption of the production event in three process modes. Third, the energy consumption information of multiple manufacturing element dimensions can be derived from the mathematical models, and therefore, the energy consumption information on multiple dimensions is easily scaled. Finally, a case of refined energy cost accounting is studied to demonstrate the feasibility of the proposed models. ? 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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  • 40.
    Tang, Ou
    et al.
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Guo, Zhengang
    Imperial Coll London, England.
    Wei, Shuoguo
    Jiangsu Prov Investment & Management Co Ltd, Peoples R China.
    Refund policies and core classification errors in the presence of customers choice behaviour in remanufacturing2021In: International Journal of Production Research, ISSN 0020-7543, E-ISSN 1366-588X, Vol. 59, no 12, p. 3553-3571Article in journal (Refereed)
    Abstract [en]

    In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturers perspective. The analysis indicates the importance of avoiding overestimating customers payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturers profits, and the improvement of inspection accuracy does not necessarily reduce the customers return of low-quality cores.

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  • 41.
    Yang, Qin
    et al.
    Sichuan Normal Univ, Peoples R China.
    Meng, Xin
    Sichuan Normal Univ, Peoples R China.
    Zhao, Huan
    Sichuan Normal Univ, Peoples R China.
    Cao, Cejun
    Chongqing Technol & Business Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Huisingh, Donald
    Univ Tennessee, TN USA.
    Sustainable operations-oriented painting process optimisation in automobile maintenance service2021In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 324, article id 129191Article in journal (Refereed)
    Abstract [en]

    Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises.

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  • 42.
    Wang, Wenke
    et al.
    Sichuan Univ, Peoples R China.
    Feng, Linyun
    Sichuan Normal Univ, Peoples R China.
    Zheng, Tao
    Sichuan Normal Univ, Peoples R China; Xichang Minzu Preschool Normal Coll, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    The sustainability of ecotourism stakeholders in ecologically fragile areas: Implications for cleaner production2021In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 279, article id 123606Article in journal (Refereed)
    Abstract [en]

    The research on ecotourism has attracted much attention in recent years with the increasing awareness of sustainable development and environmental protection. In the development and construction of ecotourism in ecologically fragile areas, however, conflicts of interest between stakeholders often negatively affect the efficiency and effectiveness of ecotourism construction. This paper applied evolutionary game theory to analyse the evolutionary stable strategies of local governments, tourism enterprises and residents in the development and construction of ecotourism in ecologically fragile areas, to explore the mechanism that influences the sustainable development policies. The evolutionary stable strategies of the game were calculated and the dynamic simulation of the model was also discussed by using a system dynamics method to analyse the stability of interaction among the stakeholders and determine an equilibrium solution. The simulation results showed that the strategic choices of the three stakeholders fluctuate repeatedly, which indicated that there was no evolutionary stability strategy in the interaction among the current stakeholders. Therefore, an optimized dynamic penalty-incentive control method was proposed to control the fluctuation, after which the simulation results showed that the optimized dynamic penalty-incentive control method can not only effectively suppress the fluctuation but also obtain an ideal evolutionary stable strategy. Then, the cooperation mode can be changed from any original status into the desired target. This research provides valuable information for the design of appropriate policies and business models to coordinate the interests of stakeholders and promote the development of ecotourism, as well as contributes to the environmental and sustainability research and practice. (c) 2020 Elsevier Ltd. All rights reserved.

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  • 43.
    Wang, Yu
    et al.
    Jinan Univ, Peoples R China.
    Li, Cheng
    Jinan Univ, Peoples R China.
    Zhang, Dongsong
    Jinan Univ, Peoples R China; Univ N Carolina, NC 28223 USA.
    Wu, Jiacong
    Jinan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    A deeper investigation of different types of core users and their contributions for sustainable innovation in a company-hosted online co-creation community2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 256, article id 120397Article in journal (Refereed)
    Abstract [en]

    Online co-creation allows companies to leverage external sources of knowledge to sustain product or service innovation. Users knowledge is regarded as such a potential source. Understanding user behaviors and innovation types is vital to improving a companys sustainable innovation. Many prior studies mainly categorized online community members into core and peripheral members based on their posting frequencies. However, little research has gone beyond that categorization and examined whether there may be different types of core members who may contribute to product or service innovation differently, especially in the context of co-creation. The objectives of this study are three-fold: (1) to identify core members of a company-hosted online co-creation community automatically by considering several dimensions of individual members, including posting behavior, the generated content, and social network features; (2) to categorize and compare the contributions of different types of core members in the community, aiming to identify community members who may play leadership roles in sustainable innovation; and (3) to investigate the influence of those different types of core members on other community members. The data collected from a company-hosted online co-creation community in China were analyzed. Through analysis, we developed a novel innovation-oriented topology of core community members consisting of eight types. Based on Practice Theory, we also explored how those different types of core community members may influence other members behavior. Finally, based on the findings, we propose strategies and guidelines for practitioners to keep different types of community members actively engaged in online co-creation and to manage sustainable innovation practice better. (C) 2020 Elsevier Ltd. All rights reserved.

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  • 44.
    Wang, Ning
    et al.
    Changan Univ, Peoples R China.
    Ren, Shan
    Xian Univ Posts & Telecommun, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Yang, Miying
    Univ Exeter, England.
    Wang, Jin
    Xian Univ Posts & Telecommun, Peoples R China.
    Huisingh, Donald
    Univ Tennessee, TN USA.
    An active preventive maintenance approach of complex equipment based on a novel product-service system operation mode2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 277, article id 123365Article in journal (Refereed)
    Abstract [en]

    The product-service system (PSS) business model has received increasing attention in equipment maintenance studies, as it has the potential to provide high value-added services for equipment users and construct ethical principles for equipment providers to support the implementation of circular economy. However, the PSS providers in equipment industry are facing many challenges when implementing Industry 4.0 technologies. One important challenge is how to fully collect and analyse the operational data of different equipment and diverse users in widely varied conditions to make the PSS providers create innovative equipment management services for their customers. To address this challenge, an active preventive maintenance approach for complex equipment is proposed. Firstly, a novel PSS operation mode was developed, where complex equipment is offered as a part of PSS and under exclusive control by the providers. Then, a solution of equipment preventive maintenance based on the operation mode was designed. A deep neural network was trained to predict the remaining effective life of the key components and thereby, it can pre-emptively assess the health status of equipment. Finally, a real-world industrial case of a leading CNC machine provider was developed to illustrate the feasibility and effectiveness of the proposed approach. Higher accuracy for predicting the remaining effective life was achieved, which resulted in predictive identification of the fault features, proactive implementation of the preventive maintenance, and reduction of the PSS providers maintenance costs and resource consumption. Consequently, the result shows that it can help PSS providers move towards more ethical and sustainable directions. (C) 2020 The Author(s). Published by Elsevier Ltd.

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  • 45.
    Guo, Zhengang
    et al.
    Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry, China; Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, China .
    Zhang, Yingfeng
    Key Laboratory of Industrial Engineering and Intelligent Manufacturing, Ministry of Industry, China; Information Technology, School of Mechanical Engineering, Northwestern Polytechnical University, China; Department of Mechanical and Energy Engineering, Southern University of Science and Technology, China .
    Lv, Jingxiang
    Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, School of Construction Machinery, Chang'an University, China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Department of Production, University of Vaasa, Finland.
    Liu, Ying
    James Watt School of Engineering, University of Glasgow, U.K..
    An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization2020In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 22, no 10, p. 6634-6645Article in journal (Refereed)
    Abstract [en]

    Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems in urban traffic. With the help of IoT technologies, online data from road segments are captured by monitoring devices, while real-time data from vehicles are collected through preinstalled sensors. Based on these data, a CPS model is constructed to depict real-time status and dynamic behavior of road segments and vehicles. An online learning data-driven model is developed to extract prior knowledge and enhance collaboration between road segments and vehicles by combining short-term traffic forecasting and real-time routing optimization. A case study based on Xi’an city is presented to demonstrate the feasibility and efficiency of the proposed method, showing a reduction in the travel time with reasonable computation time, without much compromising the travel distance and fuel consumption. This work potentially strengthens the transparency and intelligence of urban traffic systems.

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  • 46.
    Ma, Shuaiyin
    et al.
    Northwestern Polytech Univ, Peoples R China.
    Zhang, Yingfeng
    Northwestern Polytech Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Yang, Haidong
    Guangdong Univ Technol, Peoples R China.
    Lv, Jingxiang
    Changan Univ, Peoples R China.
    Ren, Shan
    Xian Univ Posts & Telecommun, Peoples R China.
    Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 274, article id 123155Article in journal (Refereed)
    Abstract [en]

    The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. (C) 2020 The Author(s). Published by Elsevier Ltd.

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  • 47.
    Li, Lianhui
    et al.
    Ningxia Key Laboratory of Intelligent Information and Big Data Processing, North Minzu University, People's Republic of China.
    Mao, Chunlei
    Nanjing Automation Institute of Water Conservancy and Hydrology, People's Republic of China.
    Lei, Bingbing
    Ningxia Key Laboratory of Intelligent Information and Big Data Processing, North Minzu University, People's Republic of China.
    Gao, Yang
    Ningxia Key Laboratory of Intelligent Information and Big Data Processing, North Minzu University, People's Republic of China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Huang, George Q.
    Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, People's Republic of China.
    Decision-making of product-service system solution selection based on integrated weight and technique for order preference by similarity to an ideal solution2020In: IET Collaborative Intelligent Manufacturing, E-ISSN 2516-8398, Vol. 2, no 3, p. 102-108Article in journal (Refereed)
    Abstract [en]

    Product-service system (PSS) solution selection is of great significance to better meet the personalised needs of customers and ensure the subsequent implementation. The problems of incomplete index system, difficulty to obtain the value of the qualitative index and unreasonable single index weighting have a significant impact on the decision-making of PSS solution selection. In response to these problems, a decision-making framework of PSS solution selection is constructed. A comprehensive index system is established from the perspectives of multiple stakeholders. Expert evaluating with the fuzzy number and multi-expert evaluation opinion combination is adopted for index value solving. Integration of objective and subjective weights is achieved based on the multi-weight information consistency model and the candidate PSS solutions are ranked by technique for order preference by similarity to an ideal solution finally. An application case of automobile PSS solution selection is given to verify the effectiveness and rationality of the constructed decision-making framework.

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  • 48.
    Yadav, Gunjan
    et al.
    VJTI, India.
    Luthra, Sunil
    State Inst Engn and Technol, India.
    Huisingh, Donald
    Univ Tennessee, TN 37996 USA.
    Mangla, Sachin Kumar
    Univ Plymouth, England.
    Narkhede, Balkrishna Eknath
    NITIE, India.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering.
    Development of a lean manufacturing framework to enhance its adoption within manufacturing companies in developing economies2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 245, article id 118726Article in journal (Refereed)
    Abstract [en]

    The urgent need to reduce negative corporate environmental impacts while enhancing their financial strength and positive societal benefits is attracting company leaders to implement various quality improvement systems such as lean manufacturing, six sigma, sustainable manufacturing, and circular economy concepts, approaches and technologies. All of these approaches are valuable, with Lean Manufacturing (LM) among the leading systems, if implemented within an appropriate framework. In that context, the objective of the authors was to document the drivers for improving implementation of LM within manufacturing companies. Implementation of LM practices is already providing competitive advantages such as improvements in product quality, productivity, worker health and safety and customer satisfaction in developed countries but has not been widely implemented in companies in developing countries. To help to enhance implementation of LM in developing countries, the authors developed a framework for enhancing the adoption of lean manufacturing processes in such companies. The hybrid Fuzzy Analytical Hierarchy Process (FAHP)- Decision Making Trial and Evaluation Laboratory (DEMATEL) tools were used as the framework to identify and to quantify the interrelationships among the drivers for implementation of LM. This hybrid approach facilitated documentation of the relative importance and priority of the thirty-one lean manufacturing drivers. The results revealed that improved shop-floor management, quality management, and manufacturing strategy drivers were among the most critical drivers, which enhance LM adoption. These findings are beneficial for company leaders and researchers working to improve environmental, economic and societal health, especially within companies in developing countries. (C) 2019 Elsevier Ltd. All rights reserved.

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  • 49.
    Zhang, Kai
    et al.
    Jinan Univ, Peoples R China.
    Qu, Ting
    Jinan Univ, Peoples R China.
    Zhou, Dajian
    Guangdong Univ Technol, Peoples R China.
    Jiang, Hongfei
    Jinan Univ, Peoples R China.
    Lin, Yuanxin
    Jinan Univ, Peoples R China.
    Li, Peize
    Jinan Univ, Peoples R China; Xian Univ Sci and Technol, Peoples R China.
    Guo, Hongfei
    Jinan Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Jinan Univ, Peoples R China.
    Li, Congdong
    Jinan Univ, Peoples R China.
    Huang, George Q.
    Jinan Univ, Peoples R China; Univ Hong Kong, Peoples R China.
    Digital twin-based opti-state control method for a synchronized production operation system2020In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 63, article id 101892Article in journal (Refereed)
    Abstract [en]

    The intelligent manufacturing strategy and customer demand have mutually promoted each other. Also, the production mode is shifting towards customized production, and more rental resources or services are introduced to the production system, therefore, the systems are becoming more complex. Compared with traditional production systems, such systems have some new features, this work calls this type of system as a synchronized production operation system (SPOS). Under such circumstances, production systems are influenced by more frequent uncertainties, and the planning-based production decision and control approach is no longer applicable. The opti-state control (OsC) method is proposed to help SPOS keep in an optimal state when uncertainties affect the system. Besides, a digital twin-based control framework (DTCF) is designed for getting the full element information needed for decision making. Based on the comprehensive information of the production system obtained by the DTCF, the OsC method is introduced to the virtual control layer to formulate the optimal target guiding the path of the system in real time through the dynamic matching mechanism (qualitative perspective). Then multi-stage synchronized control with analysis target cascading (ATC) method is used to get the local optimal state decisions (quantitative perspective). From both qualitative and quantitative aspects to ensure the system is under an optimal target path for optimal operation procedure. At last, a case study in a large-size paint making company in China is used to validate the effectiveness of the approach.

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  • 50.
    Sun, Huibin
    et al.
    Northwestern Polytech Univ, Peoples R China.
    Liu, Yang
    Linköping University, Department of Management and Engineering, Environmental Technology and Management. Linköping University, Faculty of Science & Engineering. Univ Vaasa, Finland.
    Pan, Junlin
    Northwestern Polytech Univ, Peoples R China.
    Zhang, Jiduo
    Northwestern Polytech Univ, Peoples R China.
    Ji, Wei
    Sandvik Coromant, Sweden.
    Enhancing cutting tool sustainability based on remaining useful life prediction2020In: Journal of Cleaner Production, ISSN 0959-6526, E-ISSN 1879-1786, Vol. 244, article id 118794Article in journal (Refereed)
    Abstract [en]

    As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk. (C) 2019 Elsevier Ltd. All rights reserved.

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