Discrete Multi-Objective Grey Wolf Algorithm Applied to Dynamic Distributed Flexible Job Shop Scheduling Problem with Variable Processing Times

Uncertainty in processing times is a key issue in distributed production; it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), in which the processing time follows a normal distr...

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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 5; p. 2281
Main Authors: Chen, Jiapeng, Wang, Chun, Xu, Binzi, Liu, Sheng
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Adjustment
Algorithms
Collaboration
Defective products
distributed flexible job shop
dynamic scheduling
Energy consumption
Factories
Flexibility
grey wolf algorithm
Heuristic
Job shops
Manufacturing
Mathematical models
Methods
Monte Carlo simulation
Normal distribution
Optimization
Performance evaluation
Scheduling
variable processing time
Iran
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:Uncertainty in processing times is a key issue in distributed production; it severely affects scheduling accuracy. In this study, we investigate a dynamic distributed flexible job shop scheduling problem with variable processing times (DDFJSP-VPT), in which the processing time follows a normal distribution. First, the mathematical model is established by simultaneously considering the makespan, tardiness, and total factory load. Second, a chance-constrained approach is employed to predict uncertain processing times to generate a robust initial schedule. Then, a heuristic scheduling method which involves a left-shift strategy, an insertion-based local adjustment strategy, and a DMOGWO-based global rescheduling strategy is developed to dynamically adjust the scheduling plan in response to the context of uncertainty. Moreover, a hybrid initialization scheme, discrete crossover, and mutation operations are designed to generate a high-quality initial population and update the wolf pack, enabling GWO to effectively solve the distributed flexible job shop scheduling problem. Based on the parameter sensitivity study and a comparison with four algorithms, the algorithm’s stability and effectiveness in both static and dynamic environments are demonstrated. Finally, the experimental results show that our method can achieve much better performance than other rules-based reactive scheduling methods and the hybrid-shift strategy. The utility of the prediction strategy is also validated.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app15052281