Energy-efficient job shop scheduling problem with transport resources considering speed adjustable resources

This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being consid...

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Vydané v:International journal of production research Ročník 62; číslo 3; s. 867 - 890
Hlavní autori: Fontes, Dalila B.M.M., Homayouni, Seyed Mahdi, Fernandes, João Chaves
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Taylor & Francis 01.02.2024
Taylor & Francis LLC
Predmet:
BRKGA
Complexity
Energy consumption
energy-efficient
Genetic algorithms
Integer programming
Job shop scheduling
Job shop scheduling problem
Job shops
Linear programming
Mixed integer
mixed integer linear programming model
multi-population
Pareto optimum
Resource scheduling
Tradeoffs
transport resources
Vehicles
ISSN:0020-7543, 1366-588X
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Shrnutí:This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2023.2175172