Work

Optimization of Dynamic Scheduling for Flexible Job Shops Using Multi-Agent Deep Reinforcement Learning

Year: 2025

Type: article

Abstract: This study proposes an optimization framework based on Multi-agent Deep Reinforcement Learning (MADRL), conducting a systematic exploration of FJSP under dynamic scenarios. The research analyzes the impact of two types of dynamic disturbance events—machine failures and order insertions—on the Dynamic Flexible Job Shop Scheduling Problem (DFJSP). Furthermore, it integrates process selection agents and machine selection agents to devise solutions for handling dynamic events. Experimental results demonstrate that, when solving standard benchmark problems, the proposed multi-objective DFJSP scheduling method, based on the 3DQN algorithm and incorporating an event-triggered rescheduling strategy, effectively mitigates disruptions caused by dynamic events.

Source: Processes

DOI: 10.3390/pr13124045

Authors: Jianqi Wang, Renwang Li, Qiang Wang

Institution: Zhejiang Sci-Tech University

Language: English

Topic: Scheduling and Optimization Algorithms

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Key Research and Development Program of Zhejiang Province