Modeling supply chain complexity using a distributed multi-objective genetic algorithm

Authors:
Khalid Al-Mutawah;Vincent Lee;Yen Cheung
Affiliations:
Clayton School of Information Technology, Monash University, Melbourne, Victoria, Australia;Clayton School of Information Technology, Monash University, Melbourne, Victoria, Australia;Clayton School of Information Technology, Monash University, Melbourne, Victoria, Australia
Venue:
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Year:
2006

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Abstract

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.