Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/9048
Title: Network optimization in supply chain : a KBGA approach
Authors: Prakash, A
Chan, FTS 
Liao, H
Deshmukh, SG
Keywords: Genetic Algorithm
Knowledge Based Genetic Algorithm
Knowledge Management
Supply chain
Issue Date: 2012
Publisher: Elsevier
Source: Decision support systems, 2012, v. 52, no. 2, p. 528-538 How to cite?
Journal: Decision support systems 
Abstract: In this paper, we present a Knowledge Based Genetic Algorithm (KBGA) for the network optimization of Supply Chain (SC). The proposed algorithm integrates the knowledge base for generating the initial population, selecting the individuals for reproduction and reproducing new individuals. From the literature, it has been seen that simple genetic-algorithm-based heuristics for this problem lead to and large number of generations. This paper extends the simple genetic algorithm (SGA) and proposes a new methodology to handle a complex variety of variables in a typical SC problem. To achieve this aim, three new genetic operators-knowledge based: initialization, selection, crossover, and mutation are introduced. The methodology developed here helps to improve the performance of classical GA by obtaining the results in fewer generations. To show the efficacy of the algorithm, KBGA also tested on the numerical example which is taken from the literature. It has also been tested on more complex problems.
URI: http://hdl.handle.net/10397/9048
ISSN: 0167-9236
EISSN: 1873-5797
DOI: 10.1016/j.dss.2011.10.024
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