A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems

M. Saqlain (), S. Ali () and J. Y. Lee ()
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M. Saqlain: Chungbuk National University
S. Ali: Chungbuk National University
J. Y. Lee: Chungbuk National University

Flexible Services and Manufacturing Journal, 2023, vol. 35, issue 2, No 9, 548-571

Abstract: Abstract Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with additional features of routing flexibility. It is an essential class of sequencing and planning problems that can apply in many real-life applications, especially in the field of manufacturing systems and production management. Finding a scheduling solution of sequential operations of various jobs by processing them on a defined number of machines and following various constraints with the goal to minimize the completion time of all operations, known as Makespan, is a big challenging issue. To address this issue, we proposed a Monte Carlo Tree Search-based flexible job-shop scheduling algorithm called MCTS-FJS for scheduling highly complex jobs in a real-time job-shop environment. An MCTS is a tree search technique aimed at making sequential decisions with uncertainty, calculate reward values from sub-tees, and regularly explore the most promising sub-tree. Experimental results showed that MCTS-scheduler outperformed various baseline scheduling algorithms and got the best evaluation performance for our sample dataset. More importantly, results showed that the performance of the proposed algorithm improved with increasing the number of jobs. Hence, this novel approach can be used to solve the complex FJSP in manufacturing systems.

Keywords: Flexible job-shop scheduling; Production scheduling; Monte-Carlo tree search; Reinforcement learning; Smart manufacturing (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10696-021-09437-4

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