Green Logistics Delivery Path Optimization Considering Deliveryman Satisfaction under Time-Varying Road Networks
- DOI
- 10.2991/978-94-6463-570-6_100How to use a DOI?
- Keywords
- green logistics; deliveryman satisfaction; time-varying road network
- Abstract
With the rise of green logistics and the continuous development of urban transportation networks, in order to solve the problems of high carbon emissions and low satisfaction of delivery personnel in logistics distribution. This article focuses on the optimization problem of green logistics distribution paths under time-varying road networks. With the satisfaction of delivery personnel as a constraint, carbon emission costs are considered in the distribution problem. A green logistics distribution model with the minimum comprehensive cost is established, and an improved ant colony algorithm integrating particle swarm algorithm ideas is designed. The effectiveness and feasibility of the proposed method are verified through numerical experiments. The research results indicate that green logistics delivery path optimization considering the satisfaction of delivery personnel can significantly reduce carbon emissions and improve delivery efficiency.
- 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.
Cite this article
TY - CONF AU - Yunhao Zhou AU - Tinxin Wen PY - 2024 DA - 2024/11/22 TI - Green Logistics Delivery Path Optimization Considering Deliveryman Satisfaction under Time-Varying Road Networks BT - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024) PB - Atlantis Press SP - 999 EP - 1008 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-570-6_100 DO - 10.2991/978-94-6463-570-6_100 ID - Zhou2024 ER -