Neural computing & applications, 2018-07, Vol.30 (1), p.193-209
2018
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- Autor(en) / Beteiligte
- Titel
- A hybrid differential evolution algorithm with estimation of distribution algorithm for reentrant hybrid flow shop scheduling problem
- Ist Teil von
- Neural computing & applications, 2018-07, Vol.30 (1), p.193-209
- Ort / Verlag
- London: Springer London
- Erscheinungsjahr
- 2018
- Quelle
- SpringerLink
- Beschreibungen/Notizen
- This paper proposes a reentrant hybrid flow shop scheduling problem where inspection and repair operations are carried out as soon as a layer has completed fabrication. Firstly, a scheduling problem domain of reentrant hybrid flow shop is described, and then, a mathematical programming model is constructed with an objective of minimizing total weighted completion time. Then, a hybrid differential evolution (DE) algorithm with estimation of distribution algorithm using an ensemble model (eEDA), named DE–eEDA, is proposed to solve the problem. DE–eEDA incorporates the global statistical information collected from an ensemble probability model into DE. Finally, simulation experiments of different problem scales are carried out to analyze the proposed algorithm. Results indicate that the proposed algorithm can obtain satisfactory solutions within a short time.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0941-0643eISSN: 1433-3058DOI: 10.1007/s00521-016-2692-yTitel-ID: cdi_proquest_journals_2052870334
- Format
- –
- Schlagworte
- Algorithms, Artificial Intelligence, Completion time, Computational Biology/Bioinformatics, Computational Science and Engineering, Computer Science, Computer simulation, Data Mining and Knowledge Discovery, Evolutionary algorithms, Image Processing and Computer Vision, Inspection, Job shop scheduling, Job shops, Mathematical programming, Original Article, Probability and Statistics in Computer Science, Production scheduling, Statistical analysis