Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27278
Title: A modified multi-criterion optimization genetic algorithm for order distribution in collaborative supply chain
Authors: Zhang, H
Deng, Y
Chan, FTS 
Zhang, X
Keywords: AHP
Collaborative supply chain
GA
Order distribution
TOPSIS
Issue Date: 2013
Publisher: Elsevier Science Inc
Source: Applied mathematical modelling, 2013, v. 37, no. 14-15, p. 7855-7864 How to cite?
Journal: Applied Mathematical Modelling 
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.
URI: http://hdl.handle.net/10397/27278
ISSN: 0307-904X
DOI: 10.1016/j.apm.2013.05.021
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