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A Reinforcement Learning Driven Artificial Bee Colony Algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times
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DOI：10.1109/TASE.2022.3212786.png)
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The distributed heterogeneous factory system is a typical scenario in the manufacturing industry. A distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup times (DHNWFSP-SDST) is studied in this paper. The differences in factory configuration and transportation time are considered in DHNWFSP-SDST. A mixed-integer linear programming (MILP) model is constructed and an artificial bee colony algorithm (ABC) with Q-learning (QABC) is proposed to address the DHNWFSP-SDST. Heuristic methods named NEH_H and DHHS are designed to construct potential initial candidates for the population. The neighborhood structures based on the job blocks are introduced in QABC to explore the solution space during the evolution processes. The Q-learning mechanism is employed to select neighborhood structures via empirical knowledge in the operation processes. The speed-up methods to accelerate the evaluation of the obtained neighborhood are designed to reduce the computation time of the QABC. The experimental results show that the QABC is a potential algorithm to address the DHNWFSP-SDST.
Keyword:
Heterogeneous flowshop scheduling
sequence-dependent setup times
artificial bee colony algorithm
Q-learning, neighborhood structure
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6.4
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5.2K
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