Annals of Operations Research

, Volume 107, Issue 1–4, pp 65–81 | Cite as

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

  • Waiman Cheung
  • Hong Zhou


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 |Cmax ). 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.

scheduling sequence-dependent setup time job shop genetic algorithm and heuristic 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Waiman Cheung
    • 1
  • Hong Zhou
    • 2
  1. 1.Faculty of Business AdministrationThe Chinese University of Hong KongShatinHong Kong
  2. 2.School of Economics and ManagementBeijing University of Aeronautics and AstronauticsBeijingPR China

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