Collaborative Scheduling of Production and Transportation in the Shop-Floor Based on Digital Twin

Abstract

Production and transportation are core parts of a manufacturing enterprise. However, traditional optimization of production and transportation schedule is performed separately, which reduces operational efficiency of the actual system. In this context, a collaborative scheduling mechanism of production and transportation based on digital twin (DT) is proposed. Under the digital twin framework, a collaborative three-stage scheduling model for production, distribution, and purchases was developed with the goal of reducing the time required for transportation in a flexible job shop manufacturing setting. The model is solved using an improved genetic algorithm (EGA) that the chromosomal encoding and decoding are handled in accordance with the specifics of the issue. The digital twin-based model can monitor the physical environment in real time and deal with dynamic interference like urgent insertion of orders in time. Through the case study and experiment analysis, the results demonstrate the effectiveness of the collaborative scheduling strategy and the superiority of the dynamic collaborative scheduling within the framework of digital twin.