Abstract
The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.
Highlights
The traditional flow shop scheduling problem can be described as n workpieces to be processed on m machines, each workpiece has to be machined in m machines, and each machining stage must be worked on different machines
The computing time is not given in artificial immune system (AIS) and genetic algorithm (GA) but usually tens of seconds, the to 21 s of computing time of the reinforcement learning (RL) method can meet the actual needs of production
hybrid flow-shop scheduling problem (HFSP) based on reinforcement learning is addressed in this paper
Summary
The traditional flow shop scheduling problem can be described as n workpieces to be processed on m machines, each workpiece has to be machined in m machines, and each machining stage must be worked on different machines. HFSP is the integration of traditional flow shop scheduling and parallel machine scheduling [2,3]. With the characteristics of flow shop and parallel machine, HFSP is difficult to solve and even the two-stage. HFSP is an NP-hard (non-deterministic polynomial, NP) problem [4]. In the same parallel machine HFSP, any workpiece has the same processing time on any parallel machine at each stage. The machining time of any workpiece on any parallel machine at each stage is inversely proportional to the processing speed of the machine in the uniform parallel machine
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