Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problems

A growing concern about the environmental impact of manufacturing processes and in particular the associated energy consumption has recently driven some researchers within the scheduling community to consider energy costs in addition to more traditional performance-related measures, such as satisfac...

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Vydané v:	Soft computing (Berlin, Germany) Ročník 24; číslo 21; s. 16291 - 16302
Hlavní autori:	González-Rodríguez, Inés, Puente, Jorge, Palacios, Juan José, Vela, Camino R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020 Springer Nature B.V
Predmet:	Artificial Intelligence Computational Intelligence Control Energy consumption Energy costs Energy management Engineering Evolutionary algorithms Fuzzy sets Genetic algorithms Heuristic Job shop scheduling Job shops Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Robotics Scheduling Uncertainty Variables Job shop scheduling Energy efficiency Fuzzy durations Multi-objective Genetic algorithm Due dates
ISSN:	1432-7643, 1433-7479
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Abstract	A growing concern about the environmental impact of manufacturing processes and in particular the associated energy consumption has recently driven some researchers within the scheduling community to consider energy costs in addition to more traditional performance-related measures, such as satisfaction of due-date commitments. Recent research is also devoted to narrowing the gap between real-world applications and academic problems by handling uncertainty in some input data. In this paper, we address the job shop scheduling problem, a well-known hard problem with many applications, using fuzzy sets to model uncertainty in processing times and with the target of finding solutions that perform well with respect to both due-date fulfilment and energy efficiency. The resulting multi-objective problem is solved using an evolutionary algorithm based on the NSGA-II procedure, where the decoding operator incorporates a new heuristic procedure in order to improve the solutions’ energy consumption. This heuristic is based on a theoretical analysis of the changes in energy consumption when a solution is subject to slight changes, referred to as local right shifts. The experimental results support the theoretical study and show the potential of the proposal.
AbstractList	A growing concern about the environmental impact of manufacturing processes and in particular the associated energy consumption has recently driven some researchers within the scheduling community to consider energy costs in addition to more traditional performance-related measures, such as satisfaction of due-date commitments. Recent research is also devoted to narrowing the gap between real-world applications and academic problems by handling uncertainty in some input data. In this paper, we address the job shop scheduling problem, a well-known hard problem with many applications, using fuzzy sets to model uncertainty in processing times and with the target of finding solutions that perform well with respect to both due-date fulfilment and energy efficiency. The resulting multi-objective problem is solved using an evolutionary algorithm based on the NSGA-II procedure, where the decoding operator incorporates a new heuristic procedure in order to improve the solutions’ energy consumption. This heuristic is based on a theoretical analysis of the changes in energy consumption when a solution is subject to slight changes, referred to as local right shifts. The experimental results support the theoretical study and show the potential of the proposal.
Author	Puente, Jorge Vela, Camino R. González-Rodríguez, Inés Palacios, Juan José
Author_xml	– sequence: 1 givenname: Inés orcidid: 0000-0003-3266-009X surname: González-Rodríguez fullname: González-Rodríguez, Inés organization: Department of Maths, Stats and Computing, University of Cantabria – sequence: 2 givenname: Jorge orcidid: 0000-0001-6840-3939 surname: Puente fullname: Puente, Jorge organization: Dep. of Computer Science, University of Oviedo – sequence: 3 givenname: Juan José orcidid: 0000-0002-0479-1490 surname: Palacios fullname: Palacios, Juan José organization: Dep. of Computer Science, University of Oviedo – sequence: 4 givenname: Camino R. orcidid: 0000-0001-9271-2360 surname: Vela fullname: Vela, Camino R. email: crvela@uniovi.es organization: Dep. of Computer Science, University of Oviedo
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SubjectTerms	Artificial Intelligence Computational Intelligence Control Energy consumption Energy costs Energy management Engineering Evolutionary algorithms Fuzzy sets Genetic algorithms Heuristic Job shop scheduling Job shops Mathematical Logic and Foundations Mechatronics Methodologies and Application Multiple objective analysis Robotics Scheduling Uncertainty Variables
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Title	Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problems
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