Novel Hybrid Algorithm of Cooperative Evolutionary Algorithm and Constraint Programming for Dual Resource Constrained Flexible Job Shop Scheduling Problems
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| Titel: | Novel Hybrid Algorithm of Cooperative Evolutionary Algorithm and Constraint Programming for Dual Resource Constrained Flexible Job Shop Scheduling Problems |
|---|---|
| Autoren: | Changao Li, Leilei Meng, Saif Ullah, Peng Duan, Biao Zhang, Hongyan Sang |
| Quelle: | Complex System Modeling and Simulation, Vol 5, Iss 3, Pp 236-251 (2025) |
| Verlagsinformationen: | Tsinghua University Press, 2025. |
| Publikationsjahr: | 2025 |
| Bestand: | LCC:Electronic computers. Computer science LCC:Systems engineering |
| Schlagwörter: | flexible job shop scheduling problem, worker flexibility, machine flexibility, cooperative evolution algorithm, constraint programming, Electronic computers. Computer science, QA75.5-76.95, Systems engineering, TA168 |
| Beschreibung: | In real production, machines are operated by workers, and the constraints of worker flexibility should be considered. The flexible job shop scheduling problem with both machine and worker resources (DRCFJSP) has become a research hotspot in recent years. In this paper, DRCFJSP with the objective of minimizing the makespan is studied, and it should solve three sub-problems: machine allocation, worker allocation, and operations sequencing. To solve DRCFJSP, a novel hybrid algorithm (CEAM-CP) of cooperative evolutionary algorithm with multiple populations (CEAM) and constraint programming (CP) is proposed. Specifically, the CEAM-CP algorithm is comprised of two main stages. In the first stage, CEAM is used based on three-layer encoding and full active decoding. Moreover, CEAM has three populations, each of which corresponds to one layer encoding and determines one sub-problem. Moreover, each population evolves cooperatively by multiple cross operations. To further improve the solution quality obtained by CEAM, CP is adopted in the second stage. Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations, CP, and CEAM-CP. Most importantly, the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances. |
| Publikationsart: | article |
| Dateibeschreibung: | electronic resource |
| Sprache: | English |
| ISSN: | 2096-9929 2097-3705 |
| Relation: | https://www.sciopen.com/article/10.23919/CSMS.2024.0041; https://doaj.org/toc/2096-9929; https://doaj.org/toc/2097-3705 |
| DOI: | 10.23919/CSMS.2024.0041 |
| Zugangs-URL: | https://doaj.org/article/26c53c11f43944b9aa2a8a8747897c9c |
| Dokumentencode: | edsdoj.26c53c11f43944b9aa2a8a8747897c9c |
| Datenbank: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstract: | In real production, machines are operated by workers, and the constraints of worker flexibility should be considered. The flexible job shop scheduling problem with both machine and worker resources (DRCFJSP) has become a research hotspot in recent years. In this paper, DRCFJSP with the objective of minimizing the makespan is studied, and it should solve three sub-problems: machine allocation, worker allocation, and operations sequencing. To solve DRCFJSP, a novel hybrid algorithm (CEAM-CP) of cooperative evolutionary algorithm with multiple populations (CEAM) and constraint programming (CP) is proposed. Specifically, the CEAM-CP algorithm is comprised of two main stages. In the first stage, CEAM is used based on three-layer encoding and full active decoding. Moreover, CEAM has three populations, each of which corresponds to one layer encoding and determines one sub-problem. Moreover, each population evolves cooperatively by multiple cross operations. To further improve the solution quality obtained by CEAM, CP is adopted in the second stage. Experiments are conducted on 13 benchmark instances to assess the effectiveness of multiple crossover operations, CP, and CEAM-CP. Most importantly, the proposed CEAM-CP improves 9 best-known solutions out of 13 benchmark instances. |
|---|---|
| ISSN: | 20969929 20973705 |
| DOI: | 10.23919/CSMS.2024.0041 |