Digital twin framework for large-scale optimization problems in supply chains: a case of packing problem
Journal
Mobile Networks and Applications
ISSN
1383-469X
1572-8153
Publisher
Springer
Date Issued
2021
Author(s)
Marmolejo Saucedo, José Antonio
Type
Resource Types::text::journal::journal article
Abstract
The development of new information technologies at the beginning of the 21st century allows the integration between the physical and the virtual world. In Engineering, an emerging technology called digital twins is presented as the mechanism to virtualize the operation of devices, machines and processes. In industrial engineering and specifically in supply chains there is a growing interest in the development of digital twins. For this reason, this paper proposes the integration of large-scale optimization problems in a digital platform that allows the solution of these problems for decision-making in real time. Bin-Packing and Vehicle Routing problems are addressed through the interface of a commercial supply chain management platform and heuristic optimization algorithms. We use technology based on simulation of discrete events to achieve the periodic decisions that make up the Digital Supply ChainTwin engine. A hypothetical case solution is presented to verify the performance of the proposed development. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Introduction -- Literature review -- Large-scale optimization models -- 3D-bin packing problem (3D-BPP) -- The generalized vehicle routing problem (GVRP) -- Twining data-driven joint optimization -- Case study environment -- File entry model for 3D-BPP -- Output file for 3D-BPP -- Input data for the GVRP -- Results -- Conclusions.
How to cite
Marmolejo-Saucedo, J. A. (2022). Digital twin framework for large-scale optimization problems in supply chains: A case of packing problem. Mobile Networks and Applications, 27(5), 2198–2214. https://doi.org/10.1007/s11036-021-01856-9