An estimation of distribution algorithm for the flexible job-shop scheduling problem

Authors:
Shengyao Wang;Ling Wang;Gang Zhou;Ye Xu
Affiliations:
Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China;Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, P.R. China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

In this paper, an effective estimation of distribution algorithm (EDA) is proposed to solve the flexible job-shop scheduling problem with the criterion to minimize the maximum completion time (makespan). With the framework of the EDA, the probability model is built with the superior population and the new individuals are generated based on probability model. In addition, an updating mechanism of the probability model is proposed and a local search strategy based on critical path is designed to enhance the exploitation ability. Finally, numerical simulation is carried out based on the benchmark instances, and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.