Title: Low-carbon logistics and distribution scheme for smart city by integrating internet of things technology and improved genetic hybrid algorithm

Authors: Yanyan Jin; Xia Chen; Kaiyuan Zhang

Addresses: Commerce and Logistics School, Henan Institute of Economics and Trade, Zhengzhou, 450000, China ' Commerce and Logistics School, Henan Institute of Economics and Trade, Zhengzhou, 450000, China ' Educational Arts School, Zhengzhou E-Commerce Vocational College, Zhengzhou, 450000, China

Abstract: To find the optimal path for distribution, the experiment proposes a low-carbon logistics and distribution path optimisation method for smart cities based on the internet of things (IoT) technology and ant colony genetic hybrid algorithm. On dataset A, when iterating up to 62 times, the average running time of this method (5.54 s) is less than other algorithms. When the full load factor was only 30%, the research method started to get the minimum total cost. The overall cost did not increase noticeably with the tax when the carbon tax was between 0 and 100 yuan/ton. It indicated that the businesses in this range found the carbon tax to be acceptable. The research technique yielded a shorter optimal distribution path distance of 18.69 kilometres, as demonstrated by the application comparison. These results can provide a new theoretical basis for the low-carbon logistics and distribution in the smart city.

Keywords: low carbon; logistics and distribution; ant colony algorithm; ACA; genetic algorithm; smart city; internet of things; IoT.

DOI: 10.1504/IJEE.2026.151948

International Journal of Environmental Engineering, 2026 Vol.14 No.1, pp.52 - 71

Received: 03 Apr 2025
Accepted: 27 Aug 2025
Published online: 27 Feb 2026 *

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