MILP modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times

This paper addresses the flexible job shop scheduling problem with controllable processing times (FJSP-CPT). The objective is to simultaneously minimize makespan and total energy consumption. To solve the problem, a mixed integer linear programming (MILP) model is developed, and then the epsilon met...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 82; S. 101374
Hauptverfasser: Meng, Leilei, Zhang, Chaoyong, Zhang, Biao, Gao, Kaizhou, Ren, Yaping, Sang, Hongyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.10.2023
Schlagworte:
Controllable processing times
Energy consumption
Flexible job shop scheduling problem
Mixed integer linear programming
Postponing strategy
Shuffled frog-leaping algorithm
Turning Off/On strategy
Flexible job shop scheduling problem
Energy consumption
Controllable processing times
Postponing strategy
Mixed integer linear programming
Shuffled frog-leaping algorithm
Turning Off/On strategy
ISSN:2210-6502
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Zusammenfassung:This paper addresses the flexible job shop scheduling problem with controllable processing times (FJSP-CPT). The objective is to simultaneously minimize makespan and total energy consumption. To solve the problem, a mixed integer linear programming (MILP) model is developed, and then the epsilon method is used to obtain the optimal Pareto front for small-scale instances. In order to obtain approximate Pareto fronts for medium- and large-sized problems, we propose an efficient multi-objective hybrid shuffled frog-leaping algorithm (MOHSFLA). In the proposed MOHSFLA, the encoding method, the decoding method, the initiation method of the population and the evolution processes are designed. Specifically, an energy-efficient decoding with three energy-saving strategies, namely decelerating, Turning Off/On and postponing, is designed. In addition, a multi-objective variable local search (MO-VNS) algorithm is designed and embedded in the algorithm to enhance its local exploitation capability. Finally, numerical experiments are conducted to evaluate the performances of the proposed MILP model and MOHFSLA.
ISSN:2210-6502
DOI:10.1016/j.swevo.2023.101374