Comparison of Optimization Models for Cold-chain Logistics Distribution Routes of Aquatic Products: Analysis of Ant Colony Optimization and Simulated Annealing

Yuxuan Zhang

doi:10.54254/2754-1169/2025.CAU25208

Advances in Economics, Management and Political SciencesOpen access

Comparison of Optimization Models for Cold-chain Logistics Distribution Routes of Aquatic Products: Analysis of Ant Colony Optimization and Simulated Annealing

Research Article

Open Access

Comparison of Optimization Models for Cold-chain Logistics Distribution Routes of Aquatic Products: Analysis of Ant Colony Optimization and Simulated Annealing

Yuxuan Zhang ^1*

¹ Yunnan University

^*Corresponding author: zhangyuxuan_s69e@stu.ynu.edu.cn

Published on 20 July 2025

AEMPS Vol.203

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-275-1

ISBN (Online): 978-1-80590-276-8

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Abstract

This study aims to compare the performance of the Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms in optimizing distribution routes for cold-chain logistics of aquatic products. First, a multi-objective optimization model is constructed with distribution cost, transportation time, and loss rate as core objectives, incorporating temperature control and time window constraints. Constraint integration is achieved through improved algorithm designs, such as the temperature deviation penalty term in ACO and the dynamic penalty function in SA. Second, based on a simulation scenario with 21 nodes (1 distribution center and 20 customer points), systematic comparative experiments are conducted on the two algorithms, focusing on core indicators including total driving distance, time window violation, total cost, and product loss rate. The results show that the SA algorithm performs better in comprehensive optimization indicators: the total cost is 1.87% lower than ACO, the product loss rate is reduced to 3.8%, the time window satisfaction rate is increased to 88%, and the temperature constraint violation rate is only 0.5%, making it more suitable for strict temperature control scenarios of long-distance and high-value aquatic products. The ACO algorithm, however, has an advantage in convergence speed (reaching the optimal solution in 27 iterations) and computational efficiency, suitable for dynamic route adjustment in small-to-medium distribution networks. The study suggests that future research can explore hybrid optimization strategies of the algorithms and validate the model's effectiveness in larger distribution networks, providing a quantitative basis for algorithm selection in cold-chain logistics enterprises.

Keywords:

Cold-chain logistics for aquatic products, Distribution route optimization, Ant Colony Optimization (ACO), Simulated Annealing (SA).

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References

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References

[3]. Liu, S., Yan, X. and Jin, Y. (2024) An edge-aware graph autoencoder trained on scale-imbalanced data for traveling salesman problems. Knowledge-Based Systems, 291, 111559.

[5]. Amanullah, W.F., Wahyuningsih, S. and Oktoviana, L.T. (2023) Ant colony optimization (ACO) pada job shop scheduling problem (JSSP). Jurnal MIPA Dan Pembelajarannya, 2(11), 9.

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Cite this article

Zhang,Y. (2025). Comparison of Optimization Models for Cold-chain Logistics Distribution Routes of Aquatic Products: Analysis of Ant Colony Optimization and Simulated Annealing. Advances in Economics, Management and Political Sciences,203,11-22.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets

ISBN: 978-1-80590-275-1(Print) / 978-1-80590-276-8(Online)

Editor: Florian Marcel Nuţă Nuţă, Li Chai

Conference website: https://2025.icemgd.org/CAU.html

Conference date: 20 September 2025

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.203

ISSN: 2754-1169(Print) / 2754-1177(Online)

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