An attempt to resolve no-wait flow shop scheduling problems using hybrid ant colony and whale optimization algorithms

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as...

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Veröffentlicht in:E+M ekonomie a management Jg. 27; H. 2; S. 108 - 124
Hauptverfasser: Rostamzadeh, Reza, Gholipour, Arezou, Komari Alaei, Mohammad Reza, Zavadskas, Edmundas Kazimieras, Saparauskas, Jonas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Liberec Technical University of Liberec 01.04.2024
Schlagworte:
Algorithms
Colonies & territories
Employment
Energy efficiency
Evolutionary algorithms
Footwear industry
Heuristic
Investigations
Literature reviews
Machinery
Manufacturing
Mathematical models
Mathematical optimization
Methods
Operations management
Optimization
Optimization algorithms
Research design
Scheduling
Scheduling (Management)
Testing
Whales & whaling
Iran
ISSN:1212-3609, 2336-5064
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Zusammenfassung:The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines’ assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals.
Bibliographie:ObjectType-Article-1
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ISSN:1212-3609
2336-5064
DOI:10.15240/tul/001/2024-2-007