Work

A Monte Carlo Tree Search with Reinforcement Learning and Graph Relational Attention Network for Dynamic Flexible Job Shop Scheduling Problem

Year: 2025

Type: article

Abstract: The dynamic flexible job shop scheduling problem (DFJSP) with machine faults, considering the recovery condition and variable processing time, is studied to determine the rescheduling scheme when machine faults occur in real time. The Monte Carlo Tree Search (MCTS) algorithm with reinforcement learning and the relational-enhanced graph attention network (MGRL) is presented to address the DFJSP with machine faults, considering the recovery condition and variable processing time. The MCTS with the skip-node restart strategy, which utilizes local optimal solutions found during the Monte Carlo sampling process, is designed to enhance the optimization efficiency of MCTS in real time. A relational graph attention network (RGAT), a relational-enhanced and transformer-integrated graph network in the MGRL, is designed to analyze the scheduling disjunctive graph, guide the Monte Carlo sampling method to improve sampling efficiency, and enhance the quality of MCTS optimization decisions. Experimental results demonstrate the effectiveness of the RGAT and the skip-node restart strategy. Further application analysis results show that the MGRL is optimal among all comparison methods when algorithms solve the DFJSP.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc10010009

Authors: Yu Jia, Rui Yang, Qiuyu Zhang

Institution: Lanzhou University of Technology

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: National Natural Science Foundation of China, Natural Science Foundation of Gansu Province