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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Vydané v:Evolutionary intelligence Ročník 14; číslo 4; s. 1581 - 1595
Hlavní autori: Kurniawan, Bobby, Song, Wen, Weng, Wei, Fujimura, Shigeru
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2021
Springer Nature B.V
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1864-5909
1864-5917
DOI:10.1007/s12065-020-00426-4