Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs

The rapid growth of electricity demand has led governments around the world to implement energy-conscious policies, such as time-of-use tariffs. The manufacturing sector can embrace these policies by implementing an innovative scheduling system to reduce its energy consumption. Therefore, this study...

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Vydáno v:	Evolutionary intelligence Ročník 14; číslo 4; s. 1581 - 1595
Hlavní autoři:	Kurniawan, Bobby, Song, Wen, Weng, Wei, Fujimura, Shigeru
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021 Springer Nature B.V
Témata:	Applications of Mathematics Artificial Intelligence Bioinformatics Control Electric power demand Energy consumption Energy costs Engineering Genetic algorithms Job shop scheduling Job shops Manufacturing Mathematical and Computational Engineering Mechatronics Perturbation Research Paper Robotics Searching Statistical Physics and Dynamical Systems Tariffs Time of use electricity pricing Electricity cost Time-of-use tariffs Bi-objective job shop scheduling Genetic algorithm Total weighted tardiness
ISSN:	1864-5909, 1864-5917
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Abstract	The rapid growth of electricity demand has led governments around the world to implement energy-conscious policies, such as time-of-use tariffs. The manufacturing sector can embrace these policies by implementing an innovative scheduling system to reduce its energy consumption. Therefore, this study addresses bi-objective job-shop scheduling with total weighted tardiness and electricity cost minimization under time-of-use tariffs. The problem can be decomposed into two sub-problems, operation sequencing and start time determination. To solve this problem, we propose a distributed-elite local search based on a genetic algorithm that uses local improvement strategies based on the distribution of elites. Specifically, chromosome encoding uses two lines of gene representation corresponding to the operation sequence and start time. We propose a decoding method to obtain a schedule that incorporates operation sequencing and start time. A perturbation scheme to reduce electricity costs was developed. Finally, a local search framework based on the distribution of elites is used to guide the selection of individuals and the determination of perturbation. Comprehensive numerical experiments using benchmark data from the literature demonstrate that the proposed method is more effective than NSGA-II, MOEA/D, and SPEA2. The results presented in this work may be useful for the manufacturing sector to adopt the time-of-use tariffs policy.
AbstractList	The rapid growth of electricity demand has led governments around the world to implement energy-conscious policies, such as time-of-use tariffs. The manufacturing sector can embrace these policies by implementing an innovative scheduling system to reduce its energy consumption. Therefore, this study addresses bi-objective job-shop scheduling with total weighted tardiness and electricity cost minimization under time-of-use tariffs. The problem can be decomposed into two sub-problems, operation sequencing and start time determination. To solve this problem, we propose a distributed-elite local search based on a genetic algorithm that uses local improvement strategies based on the distribution of elites. Specifically, chromosome encoding uses two lines of gene representation corresponding to the operation sequence and start time. We propose a decoding method to obtain a schedule that incorporates operation sequencing and start time. A perturbation scheme to reduce electricity costs was developed. Finally, a local search framework based on the distribution of elites is used to guide the selection of individuals and the determination of perturbation. Comprehensive numerical experiments using benchmark data from the literature demonstrate that the proposed method is more effective than NSGA-II, MOEA/D, and SPEA2. The results presented in this work may be useful for the manufacturing sector to adopt the time-of-use tariffs policy.
Author	Song, Wen Weng, Wei Kurniawan, Bobby Fujimura, Shigeru
Author_xml	– sequence: 1 givenname: Bobby orcidid: 0000-0001-5596-0402 surname: Kurniawan fullname: Kurniawan, Bobby email: b.kurniawan@fuji.waseda.jp organization: Graduate School of Information, Production, and Systems, Waseda University – sequence: 2 givenname: Wen surname: Song fullname: Song, Wen organization: Graduate School of Information, Production, and Systems, Waseda University – sequence: 3 givenname: Wei surname: Weng fullname: Weng, Wei organization: Kanazawa University – sequence: 4 givenname: Shigeru surname: Fujimura fullname: Fujimura, Shigeru organization: Graduate School of Information, Production, and Systems, Waseda University
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Keywords	Electricity cost Time-of-use tariffs Bi-objective job shop scheduling Genetic algorithm Total weighted tardiness
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Snippet	The rapid growth of electricity demand has led governments around the world to implement energy-conscious policies, such as time-of-use tariffs. The...
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SubjectTerms	Applications of Mathematics Artificial Intelligence Bioinformatics Control Electric power demand Energy consumption Energy costs Engineering Genetic algorithms Job shop scheduling Job shops Manufacturing Mathematical and Computational Engineering Mechatronics Perturbation Research Paper Robotics Searching Statistical Physics and Dynamical Systems Tariffs Time of use electricity pricing
Title	Distributed-elite local search based on a genetic algorithm for bi-objective job-shop scheduling under time-of-use tariffs
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