Automatic algorithm design of distributed hybrid flowshop scheduling with consistent sublots

The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extensio...

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Vydané v:Complex & intelligent systems Ročník 10; číslo 2; s. 2781 - 2809
Hlavní autori: Zhang, Biao, Lu, Chao, Meng, Lei-lei, Han, Yu-yan, Hu, Jiang, Jiang, Xu-chu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Cham Springer International Publishing 01.04.2024
Springer Nature B.V
Springer
Predmet:
Algorithms
Automatic algorithm design
Automation
Collaboration
Complexity
Computational Intelligence
Consistent sublots
Data Structures and Information Theory
Design
Distributed hybrid flowshop scheduling
Engineering
Heuristic methods
I/F-Race
Integer programming
Linear programming
Manufacturing
Metaheuristics
Mixed integer
Original Article
Parameter identification
Scheduling
I/F-Race
Distributed hybrid flowshop scheduling
Metaheuristics
Consistent sublots
Automatic algorithm design
ISSN:2199-4536, 2198-6053
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Shrnutí:The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extension of the distributed hybrid flowshop scheduling problem (DHFSP), called the distributed hybrid flowshop scheduling problem with consistent sublots (DHFSP_CS). To tackle this problem, a mixed integer linear programming (MILP) model is developed as a preliminary step. The NP-hard nature of the problem necessitates the use of the iterated F-Race (I/F-Race) as the automated algorithm design (AAD) to compose a metaheuristic that requires minimal user intervention. The I/F-Race enables identifying the ideal values of numerical and categorical parameters within a promising algorithm framework. An extension of the collaborative variable neighborhood descent algorithm (ECVND) is utilized as the algorithm framework, which is modified by intensifying efforts on the critical factories. In consideration of the problem-specific characteristics and the solution encoding, the configurable solution initializations, configurable solution decoding strategies, and configurable collaborative operators are designed. Additionally, several neighborhood structures are specially designed. Extensive computational results on simulation instances and a real-world instance demonstrate that the automated algorithm conceived by the AAD outperforms the CPLEX and other state-of-the-art metaheuristics in addressing the DHFSP_CS.
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ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-023-01288-w