Low-Carbon Flexible Job Shop Scheduling Problem Based on Deep Reinforcement Learning

Yimin Tang, Lihong Shen and Shuguang Han ()
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Yimin Tang: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Lihong Shen: School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China
Shuguang Han: School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China

Sustainability, 2024, vol. 16, issue 11, 1-23

Abstract: As the focus on environmental sustainability sharpens, the significance of low-carbon manufacturing and energy conservation continues to rise. While traditional flexible job shop scheduling strategies are primarily concerned with minimizing completion times, they often overlook the energy consumption of machines. To address this gap, this paper introduces a novel solution utilizing deep reinforcement learning. The study begins by defining the Low-carbon Flexible Job Shop Scheduling problem (LC-FJSP) and constructing a disjunctive graph model. A sophisticated representation, based on the Markov Decision Process (MDP), incorporates a low-carbon graph attention network featuring multi-head attention modules and graph pooling techniques, aimed at boosting the model’s generalization capabilities. Additionally, Bayesian optimization is employed to enhance the solution refinement process, and the method is benchmarked against conventional models. The empirical results indicate that our algorithm markedly enhances scheduling efficiency by 5% to 12% and reduces carbon emissions by 3% to 8%. This work not only contributes new insights and methods to the realm of low-carbon manufacturing and green production but also underscores its considerable theoretical and practical implications.

Keywords: deep reinforcement learning (DRL); flexible job shop scheduling; graph neural network (GNN); priority dispatching rules (PDRs); low-carbon strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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