A digital twin-driven multi-resource constrained location system for resource allocation

Smart manufacturing systems combine sensor systems and manufacturing processes, and they have been widely adopted in the industry to solve real production problems, help manufacturing enterprises achieve rapid decision-making, and improve manufacturing value. However, manufacturing enterprises still face huge challenges with the coexistence of continuously changing dynamic demands, collaborative scheduling of dynamic resources, precise matching of manufacturing resources, and multiple resource constraints. To address this challenge, this research combines digital twin (DT) technology to propose a smart site-selection system with dynamic resource-accurate matching characteristics based on the attributes and associations of both resource sides, supply and demand sides, and site-selection sides, which can integrate and optimize resources according to the requirements of manufacturing tasks. In addition, by establishing the discovery mechanism of bottleneck processes and resource allocation methods, generating configuration priorities, and thus reducing the solution space for resource allocation, the precise allocation of limited resources is achieved more quickly and easily, and the scheduling chaos in the parallel scheduling of multiple resources is solved and the multi-objective robust optimization model is solved by combining smart optimization algorithms. Combined with the example analysis, the results show that the smart site-selection system and multi-resource cyclic allocation mechanism proposed in this paper can collaboratively match a large amount of dynamic resources, and the utilization rate of idle manufacturing resources can be increased by 60%. This research effectively realizes the optimal allocation of multiple manufacturing resources in a resource-constrained environment and helps manufacturing enterprises create more manufacturing value.