Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Real-time Production and Logistics Self-adaption Scheduling based on Information Entropy Theory

Version 1 : Received: 5 August 2020 / Approved: 6 August 2020 / Online: 6 August 2020 (06:00:12 CEST)

A peer-reviewed article of this Preprint also exists.

Yang, W.; Li, W.; Cao, Y.; Luo, Y.; He, L. Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory. Sensors 2020, 20, 4507. Yang, W.; Li, W.; Cao, Y.; Luo, Y.; He, L. Real-Time Production and Logistics Self-Adaption Scheduling Based on Information Entropy Theory. Sensors 2020, 20, 4507.

Journal reference: Sensors 2020, 20, 4507
DOI: 10.3390/s20164507

Abstract

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Subject Areas

industrial internet of things; random job arrival time; information entropy theory; self-adaption; real-time scheduling

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