A combinatorial evolutionary algorithm for unrelated parallel machine scheduling problem with sequence and machine-dependent setup times, limited worker resources and learning effect

•A new model of NUPMSP considering worker resources and learning effect is proposed.•A combinatorial evolutionary algorithm is presented to solve the model.•The makespan and total energy consumption are optimized simultaneously.•Three effective methods are proposed to obtain high-quality initial sol...

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Vydáno v:Expert systems with applications Ročník 175; s. 114843
Hlavní autoři: Zhang, Like, Deng, Qianwang, Lin, Ruihang, Gong, Guiliang, Han, Wenwu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.08.2021
Elsevier BV
Témata:
Benchmarks
Combinatorial analysis
Completion time
Evolutionary algorithms
Genetic algorithms
Human factors
Job shops
Learning
Learning effect
Production costs
Scheduling
Sequence and machine-dependent setup times
Setup times
Taguchi methods
Unrelated parallel machine scheduling
Workers resources
Learning effect
Workers resources
Unrelated parallel machine scheduling
Sequence and machine-dependent setup times
ISSN:0957-4174, 1873-6793
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Shrnutí:•A new model of NUPMSP considering worker resources and learning effect is proposed.•A combinatorial evolutionary algorithm is presented to solve the model.•The makespan and total energy consumption are optimized simultaneously.•Three effective methods are proposed to obtain high-quality initial solutions. The existing papers on unrelated parallel machine scheduling problem with sequence and machine-dependent setup times (UPMSP-SMDST) ignore the worker resources and learning effect. Given the influence and potential of human factors and learning effect in real production systems to improve production efficiency and decrease production cost, we propose a UPMSP-SMDST with limited worker resources and learning effect (NUPMSP). In the NUPMSP, the workers have learning ability and are categorized to different skill levels, i.e., a worker’s skill level for a machine is changing with his accumulating operation times on the same machine. A combinatorial evolutionary algorithm (CEA) which integrates a list scheduling (LS) heuristic, the shortest setup time first (SST) rule and an earliest completion time first (ECT) rule is presented to solve the NUPMSP. In the experimental phase, 72 benchmark instances of NUPMSP are constructed to test the performance of the CEA and facilitate future study. The Taguchi method is used to obtain the best combination of key parameters of the CEA. The effectiveness of the LS, SST and ECT is verified based on 15 benchmark instances. Extensive experiments conducted to compare the CEA with some well-known algorithms confirm that the proposed CEA is superior to these algorithms in terms of solving accuracy and efficiency.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.114843