Research on Flexible Job Shop Scheduling Based on Improved Two-Layer Optimization Algorithm

To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematic...

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Bibliographic Details
Published in:Computers, materials & continua Vol. 78; no. 1; pp. 811 - 843
Main Authors: Liu, Qinhui, Zhu, Laizheng, Gao, Zhijie, Wang, Jilong, Li, Jiang
Format: Journal Article
Language:English
Published: Henderson Tech Science Press 2024
Subjects:
Automated guided vehicles
Completion time
Genetic algorithms
Impact analysis
Job shop scheduling
Job shops
Optimization algorithms
Particle swarm optimization
Production costs
Productivity
Resource scheduling
Resource utilization
Task scheduling
Workpieces
ISSN:1546-2226, 1546-2218, 1546-2226
Online Access:Get full text
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Summary:To improve the productivity, the resource utilization and reduce the production cost of flexible job shops, this paper designs an improved two-layer optimization algorithm for the dual-resource scheduling optimization problem of flexible job shop considering workpiece batching. Firstly, a mathematical model is established to minimize the maximum completion time. Secondly, an improved two-layer optimization algorithm is designed: the outer layer algorithm uses an improved PSO (Particle Swarm Optimization) to solve the workpiece batching problem, and the inner layer algorithm uses an improved GA (Genetic Algorithm) to solve the dual-resource scheduling problem. Then, a rescheduling method is designed to solve the task disturbance problem, represented by machine failures, occurring in the workshop production process. Finally, the superiority and effectiveness of the improved two-layer optimization algorithm are verified by two typical cases. The case results show that the improved two-layer optimization algorithm increases the average productivity by 7.44% compared to the ordinary two-layer optimization algorithm. By setting the different numbers of AGVs (Automated Guided Vehicles) and analyzing the impact on the production cycle of the whole order, this paper uses two indicators, the maximum completion time decreasing rate and the average AGV load time, to obtain the optimal number of AGVs, which saves the cost of production while ensuring the production efficiency. This research combines the solved problem with the real production process, which improves the productivity and reduces the production cost of the flexible job shop, and provides new ideas for the subsequent research.
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2023.046040