A modified multi-criterion optimization genetic algorithm for order distribution in collaborative supply chain

Haixin Zhang, Yong Deng, Tung Sun Chan, Xiaoge Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

Supply chain collaboration is prevalent in today's business model, and has been recognized to be one of the important issues in improving competition strength. Order distribution is to determine which order should be allocated to which supplier, it plays a very important role in a collaborative supply chain, because different order distribution infers different benefit under different criteria. However, there is not much research on the order distribution methodology. This paper adopted a framework of a central coordination system, which is equipped with a multi-criteria genetic optimization feature. In the previous multi-criterion optimization genetic algorithm (MCOGA), the analytic hierarchy process (AHP) is deployed to evaluate the fitness values. In this paper, a modified MCOGA is proposed based on the technique for order preference by similarity to ideal solution (TOPSIS). There are two main parts, namely, searching and evaluation, in genetic algorithms. Compared with the MCOGA, the proposed method takes the advantage of less complexity in the evaluation stage. The numerical example of order distribution is used to illustrate the efficiency of the proposed method.
Original languageEnglish
Pages (from-to)7855-7864
Number of pages10
JournalApplied Mathematical Modelling
Volume37
Issue number14-15
DOIs
Publication statusPublished - 1 Aug 2013

Keywords

  • AHP
  • Collaborative supply chain
  • GA
  • Order distribution
  • TOPSIS

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

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