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Optimization models and methods for production planning and ship scheduling in the pulp industry
Linköping University, Department of Mathematics, Optimization . Linköping University, The Institute of Technology.
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: urn:nbn:se:liu:diva-22355Local ID: 1561ISBN: 91-7373-777-1 (print)OAI: oai:DiVA.org:liu-22355DiVA: diva2:242668
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
List of papers
1. Supply chain optimization in the pulp mill industry: IP models, column generation and novel constraint branches
Open this publication in new window or tab >>Supply chain optimization in the pulp mill industry: IP models, column generation and novel constraint branches
Show others...
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.

Keyword
Branch and bound, Integer programming, Production, Scheduling, Supply chain management
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-13130 (URN)10.1016/j.ejor.2003.08.001 (DOI)
Available from: 2008-04-02 Created: 2008-04-02 Last updated: 2013-11-05
2. Supply chain optimization in pulp distribution using a rolling horizon solution approach
Open this publication in new window or tab >>Supply chain optimization in pulp distribution using a rolling horizon solution approach
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.

 

Series
LiTH-MAT-R, 2003-25
Keyword
Supply chain, Ships, Scheduling, Mixed integer programming
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-13131 (URN)
Available from: 2008-04-02 Created: 2008-04-02 Last updated: 2013-11-05
3. A genetic algorithm for a pulp distribution problem
Open this publication in new window or tab >>A genetic algorithm for a pulp distribution problem
2003 (English)Report (Other academic)
Abstract [en]

In this paper we present a genetic algorithm for the pulp distribution problem at a large pulp producer in Scandinavia. The distribution is a major part of the company's supply chain and includes transports with cargo vessels, by train and trucks and storages at terminals in port, at pulp mills and in customer locations. The problem we focus on is to find ship schedules and pulp deliveries in order to minimize the total cost of distribution.

The genetic algorithm utilizes two linear programming models. The first model optimizes all transport flows given a schedule and the second model approximates the performance of a schedule, measured in the total distribution cost. In the computational experiments we use instances from the real world and compare the results with an exact mixed integer programming approach.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 21 p.
Series
LiTH-MAT-R, ISSN 0348-2960 ; 26
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22364 (URN)1572 (Local ID)1572 (Archive number)1572 (OAI)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-11-05

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