Modeling Supply Chain Complexity Using a Distributed Multi-objective Genetic Algorithm

  • Khalid Al-Mutawah
  • Vincent Lee
  • Yen Cheung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3980)


The aim of this paper is to use a Distributed Multi-objective Genetic Algorithm (DMOGA) to model and solve a three Sub-chains model within the supply chain (SC) problem for optimality. It is widely accepted that all SC problems are characterized by decisions that can be conflicting by nature, distributed, and constrained. Modeling these complex problems using multiples objectives, constrained satisfaction, and distribution algorithms gives the decision maker a set of optimal or near-optimal solutions from which to choose. This paper discusses some literature in SC optimization, proposes the use of the DMOGA to solve for optimality in SC optimization problems, and provides the implementation of the DMOGA to a simulated hypothetical SC problem having three Sub-chains. It is then followed by a discussion on the algorithm’s performance based on simulation results.


Supply Chain Entire Supply Chain Supply Chain Optimization Traditional Genetic Algorithm Supply Chain Configuration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Khalid Al-Mutawah
    • 1
  • Vincent Lee
    • 1
  • Yen Cheung
    • 1
  1. 1.Clayton School of Information TechnologyMonash UniversityMelbourneAustralia

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