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Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem

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  • Laurent Deroussi

    (Blaise Pascal University, Clermont-Ferrand II, France)

  • David Lemoine

    (Ecole des Mines de Nantes, France)

Abstract

This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each particle of the swarm is represented by a matrix of logistic costs. A sequential approach heuristic, using Wagner-Whitin algorithm, is used to determine the associated production planning. The authors demonstrate that any solution of the MLLP can be reached by particles. The sequential heuristic is a subjective function from the particles space to the set of the production plans, which meet the customer’s demand. The authors test the DPSO Scheme on benchmarks found in literature, more specifically the unique DPSO that has been developed to solve the MLLP.

Suggested Citation

  • Laurent Deroussi & David Lemoine, 2011. "Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 2(1), pages 44-57, January.
    • Handle: RePEc:igg:jamc00:v:2:y:2011:i:1:p:44-57
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