Low Carbon Logistics Location Problem Under Multi-Vehicle Route

DOI

10.2991/978-94-6463-256-9_152How to use a DOI?

Keywords

Site-path problem; Improved K-means clustering; Quantum genetic algorithm

Abstract

In order to solve the decision-making problem of distribution center location and multi-vehicle routing optimization combination under the background of low carbon emission, a planning model aiming at the minimum logistics comprehensive cost considering carbon emission was proposed, and a two-stage heuristic algorithm was designed to solve the problem. In the first stage, the improved k-means clustering method is designed to partition and cluster the customer nodes, and then the spatial single journey partitioning algorithm is used to determine the customers served by each distribution center with the full load condition as the limit. In the second stage, the lowest comprehensive logistics cost is taken as the optimization objective, and the quantum genetic algorithm is established to solve the problem. Combined with the data of a logistics company, it is shown that compared with other existing algorithms, the algorithm proposed in this paper can effectively reduce the comprehensive cost of logistics under the premise of low carbon emissions, and provides a new way to solve the problem of site-multi-vehicle routing.

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  - Kaiwei Jia
AU  - Jue Wang
PY  - 2023
DA  - 2023/10/09
TI  - Low Carbon Logistics Location Problem Under Multi-Vehicle Route
BT  - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
PB  - Atlantis Press
SP  - 1501
EP  - 1515
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-256-9_152
DO  - 10.2991/978-94-6463-256-9_152
ID  - Jia2023
ER  -