Meta-Heuristics for Dynamic Lot Sizing: A Review and Comparison of Solution Approaches

41 Pages Posted: 24 Sep 2004

See all articles by Raf Jans

Raf Jans

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM); HEC Montreal

Zeger Degraeve

London Business School

Date Written: 24 2004 6,

Abstract

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples.

Keywords: dynamic lotsizing, algorithms, meta-heuristics, Dantzig-Wolfe decomposition, reformulations

JEL Classification: M, M11, R4, C61

Suggested Citation

Jans, Raf and Degraeve, Zeger, Meta-Heuristics for Dynamic Lot Sizing: A Review and Comparison of Solution Approaches (24 2004 6,). Available at SSRN: https://ssrn.com/abstract=594976

Raf Jans (Contact Author)

Erasmus University Rotterdam (EUR) - Rotterdam School of Management (RSM) ( email )

P.O. Box 1738
Room T08-21
3000 DR Rotterdam, 3000 DR
Netherlands

HEC Montreal ( email )

3000, Chemin de la Côte-Sainte-Catherine
Montreal, Quebec H2X 2L3
Canada
+1 514 340 6834 (Phone)

Zeger Degraeve

London Business School ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

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