Journal of System Simulation
Abstract
Abstract: A multi-objective dynamic flexible job shop scheduling problem model with machine breakdown and random jobs arrival is constructed to address the interference of dynamic events in manufacturing processing on the scheduling scheme, and a real-time scheduling method with multiobjective proximal policy optimization (MPPO) algorithm is proposed. The MPPO algorithm trains two agents, routing agent (RA) and sequencing agent (SA), for real-time scheduling and real-time processing of dynamic events. It employs a linear combination of weight vectors and reward vectors as reward signals and stores the agents' parameters for each weight vector to optimize multiple objectives. The required state information, scheduling rules, and reward signals are defined for the two agents in conjunction with the objective functions. A comparison with nine combinations of scheduling rules for dynamic scheduling problems of different scales verifies that the MPPO algorithm-trained agents have learned an appropriate scheduling policy, which can guarantee the performance of real-time scheduling and optimize all objectives.
Recommended Citation
Jiang, Quan and Wei, Jingxuan
(2024)
"Real-time Scheduling Method for Dynamic Flexible Job Shop Scheduling,"
Journal of System Simulation: Vol. 36:
Iss.
7, Article 9.
DOI: 10.16182/j.issn1004731x.joss.23-0385
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol36/iss7/9
First Page
1609
Last Page
1620
CLC
TP391.4
Recommended Citation
Jiang Quan, Wei Jingxuan. Real-time Scheduling Method for Dynamic Flexible Job Shop Scheduling [J]. Journal of System Simulation, 2024, 36(7): 1609-1620.
DOI
10.16182/j.issn1004731x.joss.23-0385
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