An Information Theory Inspired Real-Time Self-Adaptive Scheduling for Production-Logistics Resources: Framework,...

Area of Science:

• Industrial Engineering
• Operations Research
• Intelligent Manufacturing Systems

Background:

• Industrial Internet of Things and physical network systems enable real-time data utilization in production-logistics scheduling.
• Real-time data in intelligent factories is characterized by randomness (e.g., job arrivals) and fuzziness (e.g., processing times).
• Coordinating production and logistics resources in flexible workshops is a critical challenge.

Purpose of the Study:

• To establish a real-time reaction scheduling framework for dynamic cooperation of production-logistics resources.
• To determine optimal strategies for utilizing real-time information in adaptive scheduling.
• To enhance decision-making quality in intelligent factory environments.

Main Methods:

• A real-time task triggering strategy is proposed to maximize information utilization.
• A collaborative method for production-logistics resources is developed to guide information usage.
• A real-time self-adaptive scheduling algorithm based on information entropy is employed.

Main Results:

• The proposed framework effectively integrates real-time information into scheduling decisions.
• The task triggering strategy enhances the utilization of random and fuzzy data.
• The information entropy-based algorithm yields stable and feasible scheduling solutions.

Conclusions:

• The developed framework enables adaptive collaboration of production-logistics resources.
• The study provides a novel approach to real-time scheduling in intelligent factories.
• The effectiveness is validated through a practical case study.