4.6 Article

Reverse Logistics Location Based on Energy Consumption: Modeling and Multi-Objective Optimization Method

期刊

APPLIED SCIENCES-BASEL
卷 11, 期 14, 页码 -

出版社

MDPI
DOI: 10.3390/app11146466

关键词

reverse logistics location; optimization; energy consumption; benefits; modelling

资金

  1. National Natural Science Foundation of China [52075303, 51775238]
  2. Fund of Chongqing Key Laboratory of Vehicle Emission [PFJN-04]
  3. Science and Technology Research and Planning Project of Jilin Provincial Education Department [JJKH20211368KJ]

向作者/读者索取更多资源

The low-carbon economy is a major trend in global economic development and provides a rare opportunity for China's economic transformation. This study introduces a dual objective function optimization model of reverse logistics facility location, considering carbon emissions and benefits, and applies it to a case study of mobile phone recycling logistics network in Jilin Province to verify its feasibility. The results demonstrate that this approach offers an effective solution to the multi-objective optimization problem of reverse logistics location.
The low-carbon economy, as a major trend of global economic development, has been a widespread concern, which is a rare opportunity to realize the transformation of the economic way in China. The realization of a low-carbon economy requires improved resource utilization efficiency and reduced carbon emissions. The reasonable location of logistics nodes is of great significance in the optimization of a logistics network. This study formulates a double objective function optimization model of reverse logistics facility location considering the balance between the functional objectives of the carbon emissions and the benefits. A hybrid multi-objective optimization algorithm that combines a gravitation algorithm and a particle swarm optimization algorithm is proposed to solve this reverse logistics facility location model. The mobile phone recycling logistics network in Jilin Province is applied as the case study to verify the feasibility of the proposed reverse logistics facility location model and solution method. Analysis and discussion are conducted to monitor the robustness of the results. The results prove that this approach provides an effective tool to solve the multi-objective optimization problem of reverse logistics location.

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