Abstract
As a typical non-deterministic polynomial (NP)-hard combinatorial optimization problem, the hybrid flow shop scheduling problem (HFSSP) is known to be a very common layout in real-life manufacturing scenarios. Even though many metaheuristic approaches have been presented for the HFSSP with makespan criterion, there are limitations of the metaheuristic method in accuracy, efficiency, and adaptability. To address this challenge, an improved SP-MCTS (single-player Monte-Carlo tree search)-based scheduling is proposed for the hybrid flow shop to minimize the makespan considering the multi-constraint. Meanwhile, the Markov decision process (MDP) is applied to transform the HFSSP into the problem of shortest time branch path. The improvement of the algorithm includes the selection policy blending standard deviation, the single-branch expansion strategy and the 4-Rule policy simulation. Based on this improved algorithm, it could accurately locate high-potential branches, economize the resource of the computer and quickly optimize the solution. Then, the parameter combination is introduced to trade off the selection and simulation with the intention of balancing the exploitation and exploration in the search process. Finally, through the analysis of the calculated results, the validity of improved SP-MCTS (ISP-MCTS) for solving the benchmarks is proven, and the ISP-MCTS performs better than the other algorithms in solving large-scale problems.
Highlights
The flow shop scheduling problem (FSSP) has been a very active research field, since it was first proposed by Johnson [1]
The SP-Monte-Carlo tree search (MCTS) algorithm derived from MCTS is proposed to solve hybrid flow shop scheduling problem (HFSSP), and HFSSP is seen as a single-player optimization problem
The ISP-MCTS algorithm was programmed in MATLAB 2016a and run on a central processing unit (CPU)-i5-3470(3.20GHZ) with 4.0 GB Main Memory
Summary
The flow shop scheduling problem (FSSP) has been a very active research field, since it was first proposed by Johnson [1]. Proposed heuristic algorithms and two metaheuristic techniques based on an artificial immune system for a two-stage assembly hybrid flow shop scheduling problem. A MCTS-based algorithm for the multi-objective flexible job shop scheduling problem was presented by Wu et al [33], and it was improved by incorporating the Variable Neighborhood Descent Algorithm and other techniques, like rapid action value, which can estimate the heuristic and transposition table. Used the improved MCTS algorithm to solve the multi-objective flexible shop scheduling problem, and search the minimum completion time by the adaptive value game comparison. Good efforts have been made in the above studies, and MCTS is a series of two-person zero-sum game decision-making methods, there are still limitations in the performance and learning efficiency when solving shop floor scheduling problems.
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