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Supply chain optimization in pulp distribution using a rolling horizon solution approach
Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
Norwegian School of Economics and Business Administration, Bergen.
2006 (English)Report (Other academic)
Abstract [en]

In this paper we consider a combined supply chain and ship routing problem for a large pulp producer in Scandinavia. The problem concerns the distribution of pulp to customers, with route scheduling of ships as a central part of modeling. It is an operative planning problem with daily ship routing decisions over a 40 days period. The pulp supply is determined by fixed production plans, and the transport flows and storages are modeled with the requirement to satisfy the demand in a cost-optimal way. We develop a mixed integer programming model with binary variables for route usage of a vessel.

The problem is solved with a heuristic solution method, based on a rolling time horizon and a standard branch and bound algorithm. We apply the heuristic on problem instances with real world data, and compare results from reduced problem instances with the results from an exact branch and bound search. The computational experiments indicate that real world problems are solvable with the solution method and that it in many cases can be very effcient.

 

Place, publisher, year, edition, pages
2006. Vol. 17
Series
LiTH-MAT-R, 2003-25
Keyword [en]
Supply chain, Ships, Scheduling, Mixed integer programming
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-13131OAI: oai:DiVA.org:liu-13131DiVA: diva2:17883
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
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1071
Keyword
Mathematic, linear programming (MIP), production-planning
National Category
Mathematics
Identifiers
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)
Opponent
Supervisors
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.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1056
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22355 (URN)1561 (Local ID)91-7373-777-1 (ISBN)1561 (Archive number)1561 (OAI)
Presentation
2003-11-07, Glashuset, Hus B, Linköpings Universitet, Linköping, 10:15 (Swedish)
Opponent
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-11-05

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Bredström, DavidRönnqvist, Mikael

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