Local Search Genetic Algorithms for the Job Shop Scheduling Problem

  title={Local Search Genetic Algorithms for the Job Shop Scheduling Problem},
  author={Beatrice M. Ombuki-Berman and Mario Ventresca},
  journal={Applied Intelligence},
In previous work, we developed three deadlock removal strategies for the job shop scheduling problem (JSSP) and proposed a hybridized genetic algorithm for it. While the genetic algorithm (GA) gave promising results, its performance depended greatly on the choice of deadlock removal strategies employed. This paper introduces a genetic algorithm based scheduling scheme that is deadlock free. This is achieved through the choice of chromosome representation and genetic operators. We propose an… CONTINUE READING
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An evolutionary scheduling scheme based on gkGA approach for the job shop scheduling problem,

  • B. Ombuki, M. Nakamura, K. Onaga
  • IEICE Transactions on Fundamentals of Elecronics…
  • 1998
Highly Influential
9 Excerpts

The ant system: Optimization by a colony of cooperating agents,

  • M. Dorigo, V. Maniezzso, A. Corni
  • IEEE Transactions on Systems, Man and Cybernatics…
  • 1996
1 Excerpt

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