A Pareto-Based Estimation of Distribution Algorithm for Solving Multiobjective Distributed No-Wait Flow-Shop Scheduling Problem With Sequence-Dependent Setup Time

Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions...

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Veröffentlicht in:IEEE transactions on automation science and engineering Jg. 16; H. 3; S. 1344 - 1360
Hauptverfasser: Shao, Weishi, Pi, Dechang, Shao, Zhongshi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Biological system modeling
Distributed no-wait flow-shop scheduling problem (MDNWFSP)
Estimation
estimation of distribution algorithm (EDA)
Factories
Globalization
Industrial plants
Job shop scheduling
Job shops
makespan
multiobjective
Multiple objective analysis
Optimization
Parallel computers
Probabilistic models
Probability theory
Processor scheduling
Production facilities
Production scheduling
sequence-dependent setup time (SDST)
Sequential scheduling
Setup times
Statistical analysis
Surgery
Time dependence
total weight tardiness (TWT)
ISSN:1545-5955, 1558-3783
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Zusammenfassung:Influenced by the economic globalization, the distributed manufacturing has been a common production mode. This paper considers a multiobjective distributed no-wait flow-shop scheduling problem with sequence-dependent setup time (MDNWFSP-SDST). This scheduling problem exists in many real productions such as baker production, parallel computer system, and surgery scheduling. The performance criteria are the makespan and the total weight tardiness. In the MDNWFSP-SDST, several identical factories are considered with the related flow-shop scheduling problem with no-wait constraints. For solving the MDNWFSP-SDST, a Pareto-based estimation of distribution algorithm (PEDA) is presented. Three probabilistic models including the probability of jobs in empty factory, two jobs in the same factory, and the adjacent jobs are constructed. The PWQ heuristic is extended to the distributed environment to generate initial individuals. A sampling method with the referenced template is presented to generate offspring individuals. Several multiobjective neighborhood search methods are developed to optimize the quality of solutions. The comparison results show that the PEDA obviously outperforms other considered multiobjective optimization algorithms for addressing MDNWFSP-SDST.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2018.2886303