Hybrid Multi-Objective Artificial Bee Colony for Flexible Assembly Job Shop with Learning Effect

The flexible job shop scheduling problem is a typical and complex combinatorial optimization problem. In recent years, the assembly problem in job shop scheduling problems has been widely studied. However, most of the studies ignore the learning effect of workers, which may lead to higher costs than...

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Vydáno v:Mathematics (Basel) Ročník 13; číslo 3; s. 472
Hlavní autoři: Du, Zhaosheng, Li, Junqing, Li, Jiake
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.02.2025
Témata:
Algorithms
artificial bee colony
Assembly
Bees
Combinatorial analysis
Completion time
Critical path
Energy consumption
flexible assembly job shop
Genetic algorithms
Heuristic
hybrid multi-objective algorithm
Integer programming
Job shop scheduling
Job shops
Learning
learning effect
Linear programming
Mixed integer
Multiple objective analysis
Optimization
Scheduling
Simulated annealing
Swarm intelligence
Workers
ISSN:2227-7390, 2227-7390
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Shrnutí:The flexible job shop scheduling problem is a typical and complex combinatorial optimization problem. In recent years, the assembly problem in job shop scheduling problems has been widely studied. However, most of the studies ignore the learning effect of workers, which may lead to higher costs than necessary. This paper considers a flexible assembly job scheduling problem with learning effect (FAJSPLE) and proposes a hybrid multi-objective artificial bee colony (HMABC) algorithm to solve the problem. Firstly, a mixed integer linear programming model is developed where the maximum completion time (makespan), total energy consumption and total cost are optimized simultaneously. Secondly, a critical path-based mutation strategy was designed to dynamically adjust the level of workers according to the characteristics of the critical path. Finally, the local search capability is enhanced by combining the simulated annealing algorithm (SA), and four search operators with different neighborhood structures are designed. By comparative analysis on different scales instances, the proposed algorithm reduces 55.8 and 958.99 on average over the comparison algorithms for the GD and IGD metrics, respectively; for the C-metric, the proposed algorithm improves 0.036 on average over the comparison algorithms.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math13030472