Solving Multiobjective Fuzzy Job-Shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm

The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy pro...

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Vydané v:	IEEE transactions on industrial informatics Ročník 18; číslo 12; s. 8519 - 8528
Hlavní autori:	Wang, Gai-Ge, Gao, Da, Pedrycz, Witold
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.12.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive algorithms Adaptive parameter control Algorithms Ant colony optimization Completion time Delay time differential evolution Energy consumption Entropy Evolutionary algorithms Evolutionary computation fuzzy processing and date time Fuzzy sets fuzzy sets theory Genetic algorithms Human factors HyperText Markup Language Job shop scheduling Mutation Optimization Particle swarm optimization Scheduling selection mechanism Sociology Statistics
ISSN:	1551-3203, 1941-0050
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Abstract	The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F ) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization).
AbstractList	The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F ) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization).
Author	Pedrycz, Witold Gao, Da Wang, Gai-Ge
Author_xml	– sequence: 1 givenname: Gai-Ge orcidid: 0000-0002-3295-8972 surname: Wang fullname: Wang, Gai-Ge email: gaigewang@163.com organization: Department of Computer Science and Technology, Ocean University of China, Qingdao, China – sequence: 2 givenname: Da surname: Gao fullname: Gao, Da email: gaoda@stu.ouc.edu.cn organization: Department of Computer Science and Technology, Ocean University of China, Qingdao, China – sequence: 3 givenname: Witold orcidid: 0000-0002-9335-9930 surname: Pedrycz fullname: Pedrycz, Witold email: wpedrycz@ualberta.ca organization: Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
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Snippet	The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine...
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SubjectTerms	Adaptive algorithms Adaptive parameter control Algorithms Ant colony optimization Completion time Delay time differential evolution Energy consumption Entropy Evolutionary algorithms Evolutionary computation fuzzy processing and date time Fuzzy sets fuzzy sets theory Genetic algorithms Human factors HyperText Markup Language Job shop scheduling Mutation Optimization Particle swarm optimization Scheduling selection mechanism Sociology Statistics
Title	Solving Multiobjective Fuzzy Job-Shop Scheduling Problem by a Hybrid Adaptive Differential Evolution Algorithm
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