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Optimization of Fresh Food Logistics Routes for Heterogeneous Fleets in Segmented Transshipment Mode

Haoqing Sun, Manhui He (), Yanbing Gai and Jinghao Cao
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Haoqing Sun: School of Business Administration, Liaoning Technical University, Huludao 125105, China
Manhui He: Bohai Shipbuilding Vocational College, Huludao 125105, China
Yanbing Gai: School of Business Administration, Liaoning Technical University, Huludao 125105, China
Jinghao Cao: School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China

Mathematics, 2024, vol. 12, issue 23, 1-28

Abstract: To address the challenges of environmental impact and distribution efficiency in fresh food logistics, a segmented transshipment model involving the coordinated operation of gasoline and electric vehicles is proposed. The model minimizes total distribution costs by considering transportation, refrigeration, product damage, carbon emissions, and penalties for time window violations. The k-means++ clustering algorithm is used to determine transshipment points, while an improved adaptive multi-objective ant colony optimization algorithm (IAMACO) is employed to optimize the delivery routes for the heterogeneous fleet. The case study results show that compared to the traditional model, the segmented transshipment mode reduces the total distribution costs, carbon emissions, and time window penalty costs by 22.13%, 28.32%, and 41.08%, respectively, providing a viable solution for fresh food logistics companies to achieve sustainable and efficient growth.

Keywords: fresh food logistics; segmented transshipment; heterogeneous vehicle fleet; route optimization; adaptive multi-objective ant colony algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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