Zhang, L., Fu, M., Fei, T., Lim, M. K. and Tseng, M.-L. (2024) A cold chain logistics distribution optimization model: Beijing-Tianjin-Hebei region low-carbon site selection. Industrial Management and Data Systems, (doi: 10.1108/IMDS-08-2023-0558) (Early Online Publication)
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Abstract
Purpose: This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem. Design/methodology/approach: This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem. Findings: The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics. Originality/value: This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.
| Item Type: | Articles |
|---|---|
| Keywords: | Low-carbon cold chain logistics, distribution center site selection model, grey wolf optimization algorithm, artificial fish swarm algorithm. |
| Status: | Early Online Publication |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Lim, Professor Ming |
| Authors: | Zhang, L., Fu, M., Fei, T., Lim, M. K., and Tseng, M.-L. |
| College/School: | College of Social Sciences > Adam Smith Business School > Management |
| Journal Name: | Industrial Management and Data Systems |
| Publisher: | Emerald |
| ISSN: | 0263-5577 |
| ISSN (Online): | 1758-5783 |
| Published Online: | 05 April 2024 |
| Copyright Holders: | Copyright © 2024 Emerald Publishing Ltd |
| First Published: | First published in Industrial Management and Data Systems 2024 |
| Publisher Policy: | Reproduced in accordance with the copyright policy of the publisher |
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