A Novel Approach of Resource Allocation for Distributed Digital Twin Shop-Floor

Abstract

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected.

Keywords

• digital twin
• distributed manufacturing
• resource optimal allocation
• cloud–fog–edge computing
• shop-floor scheduling