Enhanced Monte‐Carlo tree search for dynamic flexible job shop scheduling with transportation time constraints

With the increasing uncertainty and complexity of industrial production, dynamic events are inevitably happened during the production process. This paper deals with the transportation time constrained dynamic flexible job shop scheduling problem with machine breakdown, new jobs arrival, jobs cancell...

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Bibliographic Details
Published in:Expert systems Vol. 42; no. 2
Main Authors: He, Zhou, Tang, Biao, Gu, Chan, Ran, Ning
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.02.2025
Subjects:
Algorithms
dynamic flexible job shop scheduling problem
enhanced Monte‐Carlo tree search algorithm
Industrial production
Job shop scheduling
Job shops
Monte Carlo simulation
Production scheduling
rescheduling
Scheduling
Searching
Simulation
transportation time constraint
ISSN:0266-4720, 1468-0394
Online Access:Get full text
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Summary:With the increasing uncertainty and complexity of industrial production, dynamic events are inevitably happened during the production process. This paper deals with the transportation time constrained dynamic flexible job shop scheduling problem with machine breakdown, new jobs arrival, jobs cancellation, and processing time change of operations. Considering the actual production situation, a makespan rescheduling model based on transportation time is established. A Monte‐Carlo tree search based algorithm is proposed to solve the dynamic flexible job shop scheduling problem such that the makespan is minimized. In order to improve the accuracy of the developed algorithm and the quality of the obtained scheduling schemes, subtree keeping policy, multi‐branching simulation, reinforcement learning, ε−greedy strategy, and expectation‐best evaluation method are employed. Moreover, a subtree pruning policy is embedded to improve the search efficiency of the developed algorithm. Simulation experiments are conducted on a series of instances of different sizes to compare the developed approach with the GA‐based scheduling approach and the Monte‐Carlo tree search based approach. The simulation results indicate that the developed approach is superior to the existing scheduling approaches in terms of the qualities of the obtained solutions.
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ISSN:0266-4720
1468-0394
DOI:10.1111/exsy.13727