Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times
Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times
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DOI：
10.1080/00207543.2016.1178406
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发表时间：
2016-04
影响因子：
9.2
通讯作者：
Jens Heger;J. Branke;T. Hildebrandt;B. Scholz-Reiter
中科院分区：
文献类型：
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作者：
Jens Heger;J. Branke;T. Hildebrandt;B. Scholz-Reiter
具有调度规则的分散调度在生产和物流的许多领域，特别是在高度复杂的制造系统中得到应用。由于调度规则仅限于其本地信息范围，因此没有规则在各种目标、场景和系统条件下优于其他规则。在本文中，我们提出了一种方法来动态调整参数的调度规则，根据当前的系统条件。通过机器学习方法估计所选规则的不同参数设置对系统性能的影响，其学习数据由初步仿真运行产生。使用一个动态的流水车间的情况下，顺序依赖的设置时间，我们证明了我们的方法是能够显着减少平均延误的工作。
Decentralised scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on the system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence-dependent set-up times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs.