Title: A genetic algorithm model for route optimisation of cold chain product transportation using vehicles

Authors: Sheng Zeng; Bing Wang; Gang Hu; Xu-sheng Hu; Xian-jun Dai

Addresses: School of Electrical Engineering, Wanjiang University of Technology, Ma'anshan, 243000, China ' School of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan, Anhui, 243000, China ' School of Electrical Engineering, Wanjiang University of Technology, Ma'anshan, 243000, China ' School of Electrical Engineering, Wanjiang University of Technology, Ma'anshan, 243000, China ' College of Life Sciences, China Jiliang University, Hangzhou, 310018, China

Abstract: Traditional cold chain logistics vehicles are suitable for short distance transportation, while long distance cold chain transportation faces more challenges; as the transportation distance increases, the time and temperature control in the cold chain link becomes more difficult. Because the driving route of the vehicle has been subject to the influence of technology, the driving route of the vehicle cannot be optimised. The traditional vehicle transportation is only for the tracking of the vehicle, the infrared sensor avoids obstacles to find the driving route of the vehicle, and the traditional driving route of the vehicle has limitations. At the same time, the genetic algorithm adds an adjustment strategy based on time window, which can effectively reduce the probability of conflict and deadlock, accelerate the convergence speed of the solution, and solve the scheme with the shortest total assembly time within the specified time. Based on the above design, in this paper, the vehicle path optimisation can shorten the transportation time, reduce the overall transportation cost, and improve the transportation efficiency.

Keywords: transportation; genetic algorithm; GA; route optimisation; path; transportation cost.

DOI: 10.1504/IJADS.2025.147260

International Journal of Applied Decision Sciences, 2025 Vol.18 No.4, pp.493 - 515

Received: 29 Nov 2023
Accepted: 10 Jan 2024
Published online: 14 Jul 2025 *

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