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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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 18; H. 12; S. 8519 - 8528
Hauptverfasser: Wang, Gai-Ge, Gao, Da, Pedrycz, Witold
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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).
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2022.3165636