Low Carbon Logistics Location Problem Under Multi-Vehicle Route

Kaiwei Jia () and Jue Wang
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Kaiwei Jia: Liaoning Technical University
Jue Wang: Liaoning Technical University

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 1501-1515 from Springer

Abstract: 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.

Keywords: Site-path problem; Improved K-means clustering; Quantum genetic algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_152

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