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.

Predictive planning is essential for sewage treatment, which ensures water security. In the context of Industry 4.0, new sensor technologies are generating large amounts of heterogeneous data from multiple sources in increasingly complex sewage treatment processes. This complexity renders traditional methods inadequate for the accurate and timely prognostication of data essential for predictive planning. To solve the challenge, this study proposes an architecture of artificial intelligence-enabled predictive planning to reduce cost and increase efficiency for sewage treatment. Within this architecture, a combination of a sparsely connected deep neural network model based on combined correlation analysis and an improved long short-term memory model based on periodicity is used to predict critical data for sewage treatment. Then, the proposed architecture is applied by using production data from the high-density pool unit of a sewage treatment plant. Results reveal that the accuracy of the data in predictive planning is 92.7 % compared with the actual data. Establishing this architecture for predictive planning provides a practical basis for the digital transformation of sewage treatment plants to automate processes, improve decision-making, reduce costs and increase operational efficiency.

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.

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.

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.

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.

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.

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.

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.

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.