Graph-based reinforced multi-objective optimization for distributed heterogeneous flexible job shop scheduling problem under nonidentical time-of-use electricity tariffs

[Display omitted] With the rapid development of industrial globalization and diversification, the application of distributed and heterogeneous systems in production scheduling has become increasingly widespread. This paper studies a distributed heterogeneous flexible job shop scheduling problem unde...

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Vydané v:	Expert systems with applications Ročník 290; s. 128428
Hlavní autori:	Zhang, Qichen, Shao, Weishi, Shao, Zhongshi, Pi, Dechang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 25.09.2025
Predmet:	Distributed heterogeneous flexible job shop scheduling problem Graph neural network Multi-objective optimization Proximal policy optimization Time-of-use electricity tariffs Proximal policy optimization Distributed heterogeneous flexible job shop scheduling problem Multi-objective optimization Graph neural network Time-of-use electricity tariffs
ISSN:	0957-4174
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Popis
Shrnutí:	[Display omitted] With the rapid development of industrial globalization and diversification, the application of distributed and heterogeneous systems in production scheduling has become increasingly widespread. This paper studies a distributed heterogeneous flexible job shop scheduling problem under nonidentical time-of-use electricity tariffs (DHFJSP-NTOU) that uniquely integrates three underexplored dimensions: heterogeneous factories with time-dependent processing capabilities, geographically varying time-of-use electricity tariffs, and dual optimization of production efficiency and energy sustainability. A mixed-integer linear programming (MILP) model for DHFJSP-NTOU is established. To solve the DHFJSP-NTOU, a graph-reinforced multi-objective optimization algorithm (GRMO) is developed, which features three innovations: a hybrid initialization strategy balancing greedy heuristics and solution diversity, a graph neural network (GNN) framework dynamically encoding operational interdependencies and factory-specific constraints, and a reinforcement learning-driven adaptive operator selection mechanism using proximal policy optimization (PPO) for intelligent search guidance. Finally, comprehensive experiments are carried out to assess the performance of both the MILP model and the components of the GRMO. The experimental outcomes indicate that the GRMO outperforms several of the most recent high-performance methods in solving the DHFJSP-NTOU problem. The structural analysis further validates that the GNN-based feature extraction enhances search efficiency compared to conventional methods. These innovations provide a new paradigm for addressing the challenges of sustainable scheduling in distributed manufacturing systems with heterogeneous resources.
ISSN:	0957-4174
DOI:	10.1016/j.eswa.2025.128428