Bierwirth C, Mattfeld D C
Department of Economics, University of Bremen, Box 330440, D-28334 Bremen, Germany.
Evol Comput. 1999 Spring;7(1):1-17. doi: 10.1162/evco.1999.7.1.1.
A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.
描述了一种适用于静态、动态和非确定性生产环境的作业车间调度通用模型。接下来，提出了一种解决作业车间调度问题的遗传算法。该算法在不同工作量情况下的动态环境中进行了测试。由此，提出了一种高效的解码程序，该程序极大地提高了调度质量。最后，在非确定性环境中对该技术进行了调度和重新调度测试。实验表明，传统的生产控制方法在合理的运行时成本下明显表现不佳。
