Specifying Critical Inputs in a Genetic Algorithm‐driven Decision Support System: An Automated Facility

Ramakrishnan Pakath, Jigish S. Zaveri

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


We present a simple scheme for the automated, iterative specification of the genetic mutation, crossover, and reproduction (usage) probabilities during run time for a specific genetic algorithm‐driven tool. The tool is intended for supporting static scheduling decisions in flexible manufacturing systems. Using a randomly generated (base) test problem instance, we first assess the method by using it to determine the appropriate levels for specific types of mutation and crossover operators. The level for the third operator, reproduction, may then be inferred. We next report on its ability to choose one or more appropriate crossovers from a set of many such operators. Finally, we compare the method's performance with that of approaches suggested in prior research for the base problem and a number of other test problems. Our experimental findings within the specific scheduling domain studied suggest that the simple method could potentially be a valuable addition to any genetic algorithmbased decision support tool. It is, therefore, worthy of additional investigations.

Original languageEnglish
Pages (from-to)749-771
Number of pages23
JournalDecision Sciences
Issue number6
StatePublished - Nov 1995


  • MIS/DSS and Simulation

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation


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