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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Bayesian Statistical Inference-Based Estimation of Distribution Algorithm for the Re-entrant Job-Shop Scheduling Problem with Sequence-Dependent Setup Times
verfasst von : Shao-Feng Chen, Bin Qian, Bo Liu, Rong Hu, Chang-Sheng Zhang
Erschienen in: Intelligent Computing Methodologies
Verlag: Springer International Publishing
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In this paper, a bayesian statistical inference-based estimation of distribution algorithm (BEDA) is proposed for the re-entrant job-shop scheduling problem with sequence-dependent setup times (RJSSPST) to minimize the maximum completion time (i.e., makespan), which is a typical NP hard combinatorial problem with strong engineering background. Bayesian statistical inference (BSI) is utilized to extract sub-sequence information from high quality individuals of the current population and determine the parameters of BEDA’s probabilistic model (BEDA_PM). In the proposed BEDA, BEDA_PM is used to generate new population and guide the search to find promising sequences or regions in the solution space. Simulation experiments and comparisons demonstrate the effectiveness of the proposed BEDA.