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Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling
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DOI：10.1109/TETCI.2021.3098354.png)
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As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.
Keyword:
Optimization
Job shop scheduling
Decoding
Reinforcement learning
Dynamic scheduling
Encoding
Computational intelligence
Reinforcement learning
deep neural network
flow-shop scheduling
optimization algorithm
improvement strategy
期刊
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6.5
论文数:
1.4K
被引数:
4.5K
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