An automatic multi-objective evolutionary algorithm for the hybrid flowshop scheduling problem with consistent sublots

Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots, aiming to simultaneously optimize two conflicting objectives: the mak...

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Vydáno v:Knowledge-based systems Ročník 238; s. 107819
Hlavní autoři: Zhang, Biao, Pan, Quan-ke, Meng, Lei-lei, Lu, Chao, Mou, Jian-hui, Li, Jun-qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 28.02.2022
Elsevier Science Ltd
Témata:
Algorithms
Automatic algorithm design
Automation
Consistent sublots
Decoding
Design
Encoding
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Hybrid flowshop scheduling
Integer programming
Job shops
Lot streaming
Mixed integer
Multi-objective evolutionary algorithm
Neighborhoods
Objectives
Operators
Parameters
Racing
Scheduling
Lot streaming
Hybrid flowshop scheduling
Multi-objective evolutionary algorithm
Consistent sublots
Automatic algorithm design
ISSN:0950-7051, 1872-7409
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Shrnutí:Lot streaming is the most widely used technique to facilitate overlapping of successive operations. Inspired by real-world scenarios, this paper studies a multi-objective hybrid flowshop scheduling problem with consistent sublots, aiming to simultaneously optimize two conflicting objectives: the makespan and total number of sublots. Considering the setup and transportation operations, a multi-objective mixed integer programming model is developed and the trade-off between the two objectives is evaluated. Because of the NP-hard property of the addressed problem, metaheuristics are suggested. It is well known that the performance of metaheuristics is highly dependent on the setting of algorithmic parameters, referred to as numerical and categorical parameters. However, the traditional design process might be biased by previous experience. To eliminate these issues, an automated algorithm design (AAD) methodology is introduced to conceive a multi-objective evolutionary algorithm (MOEA) in a promising framework. The AAD enables designing the algorithm by automatically determining parameters and their combinations with minimal user intervention. With regard to the problem-specific characteristics and the employed algorithm framework, for the categorical parameters, including decomposition, solution encoding and decoding, solution initialization and neighborhood structures, several operators are designed specifically. Along with the numerical parameters, these categorical parameters are determined and combined using the designed iterated racing procedure. Comprehensive computational results demonstrate that the automated MOEA outperforms other state-of-the-art MOEAs for the addressed problem.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107819