An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learn...

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Bibliographic Details
Published in:Machines (Basel) 2024-10, Vol.12 (10), p.721
Main Authors: Lu, Yongxin, Yuan, Yiping, Sitahong, Adilanmu, Chao, Yongsheng, Wang, Yunxuan
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Clean energy
Completion time
Deep learning
deep reinforcement learning
Efficiency
Energy conservation
Energy consumption
energy-saving strategy
Evolutionary algorithms
Forecasts and trends
Heuristic
Integer programming
Job shop scheduling
Job shops
Learning strategies
Machine learning
Manufacturing
Mathematical optimization
Methods
MOEA/D algorithm
multi-objective optimization
Neural networks
Optimization
permutation flow shop scheduling
Permutations
Scheduling
Search algorithms
Traveling salesman problem
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Summary:This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learning (DRL) with the multi-objective evolutionary algorithm based on decomposition (MOEA/D), termed GDRL-MOEA/D. To improve the quality of solutions, the study first employs DRL to model the PFSP as a sequence-to-sequence model (DRL-PFSP) to obtain relatively better solutions. Subsequently, the solutions generated by the DRL-PFSP model are used as the initial population for the MOEA/D, and the proposed job postponement energy-saving strategy is incorporated to enhance the solution effectiveness of the MOEA/D. Finally, by comparing the GDRL-MOEA/D with the MOEA/D, NSGA-II, the marine predators algorithm (MPA), the sparrow search algorithm (SSA), the artificial hummingbird algorithm (AHA), and the seagull optimization algorithm (SOA) through experimental tests, the results demonstrate that the GDRL-MOEA/D has a significant advantage in terms of solution quality.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines12100721