A multi-class teaching–learning-based optimization for multi-objective distributed hybrid flow shop scheduling
Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string...
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| Vydané v: | Knowledge-based systems Ročník 263; s. 110252 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
05.03.2023
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| Predmet: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP.
•MDHFSP with SDST is considered.•A TLBO with reward and punishment mechanism is proposed.•Multiple classes are formed and each of them evolves independently.•Different search times and learning phase are implemented on different classes.•Two search periods are used and teacher’s learning phase is added in each period. |
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| ISSN: | 0950-7051 1872-7409 |
| DOI: | 10.1016/j.knosys.2023.110252 |