Title: Study on cold chain logistics vehicle path optimisation method based on improved Artificial Bee Colony Algorithm

Authors: Fengju Cheng; Jingzhao Zhang

Addresses: School of International Business, Qingdao Huanghai University, Qingdao, Shandong, 266427, China ' Shandong GEO-Surveying & Mapping Institute, Jinan, Shandong, 250000, China

Abstract: Cold chain logistics (CCL) describes the process of transporting and storing perishable items from their point of manufacture to the final customer. Transportation and storage facilities equipped with refrigeration systems have been utilised. Utilising cold chain logistics, perishable foods such processed foods, meats, seafood, ice creams, poultry, dairy products, vegetables, and fruits can be safely transported from the producer to the consumer. Effective planning of the cold chain logistics vehicle is crucial for minimising travel time, distance, and overall logistics costs in order to get the product to the consumer. One such artificial swarm intelligence technique is the Artificial Bee Colony Algorithm (ABC), which is inspired by the activities of bees and their colonies. The fundamental aim of this research is to reduce the time, distance and cost associated in transportation. The results show that effective cold logistics transportation and optimal path selection have been achieved with a 98.83% success rate.

Keywords: cold chain logistics; path optimisation; artificial intelligence; transportation; artificial bee colony.

DOI: 10.1504/IJHVS.2025.148179

International Journal of Heavy Vehicle Systems, 2025 Vol.32 No.4, pp.457 - 474

Received: 24 May 2023
Accepted: 06 Sep 2023
Published online: 28 Aug 2025 *

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