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Supply chain optimization in the pulp mill industry: IP models, column generation and novel constraint branches
Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
Södra Cell AB, Växjö, Sweden.
Linköping University, Department of Science and Technology. Linköping University, The Institute of Technology.
Department of Engineering Science, The University of Auckland, Auckland, New Zealand.
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2004 (English)In: European Journal of Operational Research, ISSN 0377-2217, Vol. 156, no 1, 2-22 p.Article in journal (Refereed) Published
Abstract [en]

We study the supply chain problem of a large international pulp producer with five pulp mills located in Scandinavia. The company currently uses manual planning for most of its supply chain, which includes harvesting and transportation of pulp, production scheduling and distribution of products to customers. We have developed two new mixed integer models that determine daily supply chain decisions over a planning horizon of three months. One model is based on column generation, where the generation phase is to find new production plans using a shortest path network. The second, slightly less flexible, has the daily production decisions explicitly included in the model. In order to solve the models within practical time limits we use a flexible approach that aggregates together the less immediate decisions. We also introduce a novel constraint branching heuristic. The models and solution approaches are intended to become an integrated component in the company’s new management system. In tests and comparisons with today’s manual planning, we have found new strategic policies that significantly reduce the company’s supply chain costs.

Place, publisher, year, edition, pages
2004. Vol. 156, no 1, 2-22 p.
Keyword [en]
Branch and bound, Integer programming, Production, Scheduling, Supply chain management
National Category
URN: urn:nbn:se:liu:diva-13130DOI: 10.1016/j.ejor.2003.08.001OAI: diva2:17882
Available from: 2008-04-02 Created: 2008-04-02 Last updated: 2013-11-05
In thesis
1. Models and solution methods for large-scale industrial mixed integer programming problems
Open this publication in new window or tab >>Models and solution methods for large-scale industrial mixed integer programming problems
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with large-scale industrial problems that can be formulated using mixed integer linear programming (MIP) models. Because of the large problem size, it is not often possible to apply standard solution methods. Therefore special techniques must be used. In this thesis, both full optimization and optimization based heuristic approaches are developed.

The body of this thesis consists of five research papers. In the papers we consider industrial cases based on three applications: production planning at Södra Cell AB, ship routing and distribution at Södra Cell AB, and staff routing and scheduling in the Swedish home care.

We develop two large-scale MIP models for the production-planning problem. For solving, we use both a direct approach, and a combination of column generation and constraint branching. The latter is a technique to control the branching rules in a branch and bound framework and takes into account application specific properties.

For the ship routing problem, we present a MIP model and develop two solution methods. The first is based on an iterative rolling time horizon. In each step a part of the model is solved and later fixed. This is repeated until the full problem is solved. The second approach is to combine a genetic algorithm and linear programming. From the MIP model, we obtain solution bounds rarely present in genetic algorithm frameworks.

In the staff routing problem there are special synchronization constraints and we introduce a new MIP model. An exact solution approach based on column generation and branch and bound is developed. We also suggest a variable fixing heuristic, which we use for solving the more general problem with requirements on equal workload.

In the computational experiments, we use data instances obtained from real-world cases, and generated instances based on real-world cases. The numerical results show that high quality solutions can be obtained within reasonable time limits in all applications.

Place, publisher, year, edition, pages
Matematiska institutionen, 2007
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1071
Mathematic, linear programming (MIP), production-planning
National Category
urn:nbn:se:liu:diva-11454 (URN)978-91-85715-89-3 (ISBN)
Public defence
2007-02-16, Glashuset, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Available from: 2008-04-02 Created: 2008-04-02 Last updated: 2009-04-24
2. Optimization models and methods for production planning and ship scheduling in the pulp industry
Open this publication in new window or tab >>Optimization models and methods for production planning and ship scheduling in the pulp industry
2003 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To use optimization models and methods is an increasingly acknowledged element of supply chain management for large industries. Concurrently with the increasing global market competition and modern business policies, an already effective organization needs to find new possibilities to improve their planning procedures and resource utilization. To incorporate the planning in a more comprehensive context, in an overall supply chain, provides new possibilities to efficiently manage several planning steps than if considered individually.

This thesis considers production planning and ship scheduling problems at Södra Cell, a large Scandinavian pulp manufacturer. The main purpose of the thesis is to suggest optimization models and methods that can support production and distribution planners to objectively consider the supply chain impact in the short-term decision making process. The production planning and the ship scheduling problems are approached separately. Optimization models are formulated for both problems, and specially adapted solution methods are developed that account the characteristics of the problems.

The thesis consists of three research papers. In the first paper two mixed integer models (MIPs) are developed for the production planning problem. The first model has binary variables for every possible production plan and a column generation approach with a constraint branching heuristic is used to solve the otherwise very large model. The second model has the production choices formulated in constraints and is solved with a standard branch and bound search. The results are compared with manual planning and potential large savings are indicated.

The second and the third paper both consider the pulp distribution problem where ship routing and scheduling decisions have a key role. In the second paper the problem is solved with a rolling time horizon method. An MIP model is solved repeatedly over short ranges of time until eventually the whole planning period is covered. The third paper presents a genetic algorithm with integrated linear programming models for the distribution problem. The solutions from the different solution strategies for the distribution problem are mutually compared in the third paper.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 12 p.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1056
National Category
urn:nbn:se:liu:diva-22355 (URN)1561 (Local ID)91-7373-777-1 (ISBN)1561 (Archive number)1561 (OAI)
2003-11-07, Glashuset, Hus B, Linköpings Universitet, Linköping, 10:15 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-11-05

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Bredström, DavidLundgren, Jan T.Rönnqvist, Mikael
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