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Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop
Liping Zhang1,2, Yifan Hu1,2, Qiuhua Tang1,2
1Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel, Switzerland)
|July 24, 2021
Summary
This study introduces a closed-loop scheduling framework for intelligent manufacturing, enhancing real-time decision-making. The novel approach effectively optimizes production processes, outperforming existing methods in key performance metrics.
Area of Science:
- Manufacturing Engineering
- Artificial Intelligence
- Operations Research
Background:
- Modern manufacturing requires real-time decision-making to handle production complexities.
- Existing scheduling methods struggle with dynamic and uncertain shop floor environments.
Purpose of the Study:
- To propose a novel closed-loop scheduling framework for intelligent manufacturing.
- To enable real-time decision-making by dynamically selecting data-driven dispatching rules.
Main Methods:
- Developed a closed-loop framework with offline training and online decision-making components.
- Utilized an improved gene expression program (IGEP) for discovering dispatching rules from historical data.
- Implemented the framework in an intelligent job shop simulation with random job arrivals.
Main Results:
- The proposed framework successfully identified appropriate dispatching rules.
- The system demonstrated superior performance compared to traditional and metaheuristic-based rules.
- Significant improvements were observed in makespan, total flow time, and tardiness.
Conclusions:
- The closed-loop scheduling framework enhances real-time decision-making in intelligent manufacturing.
- The IGEP-driven approach effectively discovers robust dispatching rules.
- This method offers a significant advancement over existing scheduling strategies.
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