Optimisation of Cold Chain Logistics Paths for Fresh Produce Considering Carbon Emissions
- DOI
- 10.2991/978-94-6463-845-5_28How to use a DOI?
- Keywords
- low carbon; path optimization; ant colony algorithm; cold chain logistics
- Abstract
With the logistic challenges brought by the growth in demand for fresh agricultural products and the perishable characteristics, the optimisation of the cold chain logistics system and distribution paths has become the key to the industry. In this study, a multi-objective optimisation model integrating economic cost, carbon dioxide emission, time window constraints and carbon trading mechanism is constructed to address the problems of high cost and high carbon emission of cold chain distribution. Experiments based on the Solomon dataset show that the improved ant colony algorithm reduces the total cost by 12.7% while reducing carbon emissions by 15.3% compared with the traditional method, which verifies the effectiveness of the model in balancing economic and environmental benefits. The research results provide decision support for fresh food logistics enterprises to achieve green and sustainable development.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Nan Yang AU - Yanju Zhang PY - 2025 DA - 2025/09/16 TI - Optimisation of Cold Chain Logistics Paths for Fresh Produce Considering Carbon Emissions BT - Proceedings of the 2025 6th International Conference on Management Science and Engineering Management (ICMSEM 2025) PB - Atlantis Press SP - 263 EP - 272 SN - 2667-1271 UR - https://doi.org/10.2991/978-94-6463-845-5_28 DO - 10.2991/978-94-6463-845-5_28 ID - Yang2025 ER -