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Improved heuristically guided genetic algorithm for the flow shop scheduling problem

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  • Dipak Laha
  • Purnendu Mandal

Abstract

This paper deals with the problem of scheduling on makespan criterion in the flow shop environment. We have presented a new heuristic genetic algorithm (NGA) that combines the good features of both the genetic algorithms and heuristic search. The NGA is run on a large number of problems and its performance is compared with that of the Standard Genetic Algorithm (SGA) and the well-known Nawaz-Enscore-Ham (NEH) heuristic. The NGA is seen to perform better in almost all instances. The complexity of the NGA is found to be better than that of the SGA. The NGA also performs superior results when compared with the simulated annealing from the literature.

Suggested Citation

  • Dipak Laha & Purnendu Mandal, 2007. "Improved heuristically guided genetic algorithm for the flow shop scheduling problem," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 3(3), pages 316-331.
    • Handle: RePEc:ids:ijsoma:v:3:y:2007:i:3:p:316-331
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    Cited by:

    1. Kalczynski, Pawel J. & Kamburowski, Jerzy, 2009. "An empirical analysis of the optimality rate of flow shop heuristics," European Journal of Operational Research, Elsevier, vol. 198(1), pages 93-101, October.

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