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

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Abstract

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.

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Funding

This work was supported by the National Natural Science Foundation of China (grant number 71801160); Liaoning Provincial Education Department Scientific Research Project Funding (grant number WJGD2019002); Liaoning Provincial Social Science Foundation (grant number L16BGL035); Special Project for Humanities and Social Science Bases of Education Department (grant number ZJ2015037); Liaoning Provincial Education Science General Subject (grant number JG20DB342); Project of Liaoning Provincial Department of Education (grant number LJKR0076); and Shenyang Social Science Project (grant number SYSK2022-JD-02). The authors would like to acknowledge the funding support from Shenyang University of Technology, which is associated with this work.

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Author notes

  1. Qi Tang and Baotong Wu contributed equally to this work.

Authors and Affiliations

  1. School of Management, Shenyang University of Technology, No.111, Shenliao West Road, Liaoning Province, Shenyang, 110870, People’s Republic of China

    Qi Tang & Baotong Wu

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  1. Qi Tang
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  2. Baotong Wu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Baotong Wu. The first draft of the manuscript was written by Baotong Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Baotong Wu.

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Tang, Q., Wu, B. A digital twin-driven multi-resource constrained location system for resource allocation. Int J Adv Manuf Technol 130, 4359–4385 (2024). https://doi.org/10.1007/s00170-023-12886-w

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