Research on Low-Carbon Fresh Produce Logistics Route Optimization Based on an Improved Particle Swarm Algorithm

DOI

10.2991/978-94-6463-570-6_104How to use a DOI?

Keywords

Fresh Produce Logistics; Low-Carbon; Electric Vehicle Delivery; Route Optimization; Improved Particle Swarm Algorithm

Abstract

In the context of striving to enhance the efficiency of fresh produce logistics distribution and achieving energy saving and emission reduction goals, this paper delves into the optimization of fresh produce logistics routes based on electric vehicles. Considering the unique requirements of fresh produce delivery, the paper comprehensively examines factors such as transportation costs, carbon emissions, refrigeration effects, goods damage, and time window constraints to construct an optimization model aimed at minimizing total costs. Compared to existing literature, this study particularly emphasizes a thorough consideration of the costs associated with goods damage, aiming to ensure high precision in the model through more detailed and comprehensive analysis. To solve the model, an improved particle swarm algorithm is introduced. The effectiveness of the optimization model and algorithm is validated using the Solomon dataset. Experimental results indicate that the model performs well in reducing total costs and enhancing delivery efficiency. Specifically, it achieved an average reduction of 14.52% in total costs, a 41.15% decrease in carbon emissions, and a significant reduction in time window violations, averaging a 30.83% decrease.

Copyright

© 2024 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.

Download article (PDF)

TY  - CONF
AU  - Yanbing Gai
AU  - Liying Li
PY  - 2024
DA  - 2024/11/22
TI  - Research on Low-Carbon Fresh Produce Logistics Route Optimization Based on an Improved Particle Swarm Algorithm
BT  - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
PB  - Atlantis Press
SP  - 1035
EP  - 1047
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-570-6_104
DO  - 10.2991/978-94-6463-570-6_104
ID  - Gai2024
ER  -