Network optimization in supply chain: A KBGA approach

A. Prakash, Tung Sun Chan, H. Liao, S. G. Deshmukh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)528-538
Number of pages11
JournalDecision Support Systems
Issue number2
Publication statusPublished - 1 Jan 2012


  • Genetic Algorithm
  • Knowledge Based Genetic Algorithm
  • Knowledge Management
  • Supply chain

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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