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Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times

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Abstract

The importance of job shop scheduling as a practical problem has attracted the attention of many researchers. However, most research has focused on special cases such as single machine, parallel machine, and flowshop environments due to the “hardness” of general job shop problems. In this paper, a hybrid algorithm based on an integration of a genetic algorithm and heuristic rules is proposed for a general job shop scheduling problem with sequence-dependent setups (Jm|s jk |C max ). An embedded simulator is employed to implement the heuristic rules, which greatly enhances the flexibility of the algorithm. Knowledge relevant to the problem is inherent in the heuristic rules making the genetic algorithm more efficient, while the optimization procedure provided by the genetic algorithm makes the heuristic rules more effective. Extensive numerical experiments have been conducted and the results have shown that the hybrid approach is superior when compared to recently published existing methods for the same problem.

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Authors and Affiliations

  1. Faculty of Business Administration, The Chinese University of Hong Kong, Shatin, Hong Kong

    Waiman Cheung

  2. School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing, 100083, PR China

    Hong Zhou

Authors
  1. Waiman Cheung
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  2. Hong Zhou
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Cheung, W., Zhou, H. Using Genetic Algorithms and Heuristics for Job Shop Scheduling with Sequence-Dependent Setup Times. Annals of Operations Research 107, 65–81 (2001). https://doi.org/10.1023/A:1014990729837

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