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Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory
Wenchao Yang1, Wenfeng Li1, Yulian Cao2
1School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.
Sensors (Basel, Switzerland)
|August 19, 2020
Summary
This study introduces a new algorithm for real-time collaboration between production and logistics resources in factories facing small-batch orders. The proposed method enhances manufacturing competitiveness by improving job shop scheduling with random job arrivals.
Area of Science:
- Operations Research
- Manufacturing Systems Engineering
- Industrial Internet of Things
Background:
- Increasing customer demand for small batches and diversified orders challenges traditional manufacturing.
- Enabling technologies like the industrial Internet of Things (IIoT) and cloud manufacturing (CMfg) improve requirement elicitation and process control.
- Job shop scheduling with random job arrival times complicates production and logistics management.
Purpose of the Study:
- To develop a real-time collaborative scheduling approach for production and logistics resources.
- To address the challenges of managing diverse orders and random job arrivals in a factory setting.
- To enhance enterprise competitiveness through improved shop floor resource management.
Main Methods:
- A real-time model for production and logistics resources was established.
- A task entropy model was developed using task information.
- Real-time self-adaptive collaboration between production and logistics resources was achieved.
Main Results:
- The proposed algorithm was evaluated using a practical case study.
- Experimental results demonstrated the effectiveness of the developed algorithm.
- The proposed algorithm outperformed three existing scheduling algorithms.
Conclusions:
- The developed real-time self-adaptive collaboration model effectively addresses the complexities of modern manufacturing.
- The algorithm provides a significant improvement over existing methods for job shop scheduling.
- This research contributes to enhanced enterprise competitiveness in dynamic manufacturing environments.
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