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A Reinforcement Learning Approach to Solving Hybrid Flexible Flowline Scheduling Problems Authors: B. Van Vreckem, D. Borodin, W. De Bruyn and A. Nowé Publication Year: 2013 Pages: 402-409
Abstract: In this paper, we present a method based on Learning Automata to solve Hybrid Flexible Flowline Scheduling Problems (HFFSP) with additional constraints like sequence dependent setup times, precedence relations between jobs and machine eligibility. This category of production scheduling problems is noteworthy because it involves several types of constraints that occur in complex real-life production scheduling problems like those in process industry and batch production. In the proposed technique, Learning Automata play a dispersion game to determine the order of jobs to be processed in a way that makespan is minimized, and precedence constraint violations are avoided. Experiments on a set of benchmark problems indicate that this method can yield better results than the ones known until now.
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