Simulation optimisation of a sustainable copper mining closed-loop supply chain
This paper addresses the problem of designing a supply chain (SC) according to sustainability aspects. It identifies a research gap where an optimisation model to address the location, inventory and transportation decision in a sustainable SC applied to a copper mining industry by complementarily us...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad Politécnica de Cartagena(UPCT) |
| Repositorio: | Repositorio Digital UPCT |
| OAI Identifier: | oai:repositorio.upct.es:10317/13483 |
| Acceso en línea: | http://hdl.handle.net/10317/13483 https://www.tandfonline.com/doi/full/10.1080/23302674.2024.2311285 |
| Access Level: | acceso abierto |
| Palabra clave: | Closed-loop supply chain sustainability optimisation simulation copper mining industry Organización de Empresas |
| Sumario: | This paper addresses the problem of designing a supply chain (SC) according to sustainability aspects. It identifies a research gap where an optimisation model to address the location, inventory and transportation decision in a sustainable SC applied to a copper mining industry by complementarily using a simulation model to assess SC performance in different scenarios is needed. For this purpose, we propose a simulation model based on system dynamics (SD) to assess SC performance and to support decision making. The basis of the simulation model structure is a multi-objective mixed-integer linear programming model that aims to minimise total economic, emissions and social costs, and to also contemplate social impacts. We consider a real copper mining industry problem to illustrate this. We obtain a solution using a mathematical programming model and a simulation model. The optimisation results show improved SC performance in cost and emission reduction terms, and an improved social impact. The simulation model presents a near-optimal result and allows the possibility of anticipating adverse scenarios. Future research is oriented to other real-world applications, and to: consider alternative inventory policies; contemplate a stochastic approach; add new production and routing decisions; develop a hybrid multi-agent SD model. |
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