Robotic Digital Twin Based Full Lifecycle Decision Framework for Automotive Productions

This is the authors' abstract. We don't add key points for this paper.

Digital industrialization helps to solve the conflicts between sales, production, inventory, and supply that enterprises currently face. For example, various processes such as sales, production, warehousing, and logistics operate as isolated islands, making it difficult to coordinate with each other. Information asymmetry between customers, suppliers, and distributors leads to a lack of production line flexibility, low inventory utilization rate, and delayed decision-making. Regarding these issues, this article proposes a robotic digital twin based full lifecycle decision framework for real-time automotive production. It addresses the issues related to the generation of data to the execution of decisions across the five dimensions of data integration, transmission network, model construction, analysis methods, and provided services. This article also proposes a communication-aware generic-tabu search algorithm for real-time production capacity allocation, aiming to enhance the control, perception, and planning capabilities of robotic tasks. The framework can not only monitor real-time production but also provide decision-making recommendations in business operations that can significantly improve management efficiency and avoid risks.