Title: Intelligent cigarette loading optimisation for cost-effectiveness and low-carbon logistics

Authors: Yi Lu; Zixia Chen; Hao Chen; Xiaoping Chen

Addresses: International Business School, Zhejiang Guangsha Vocational and Technical University of Construction, No. 1, Guangfu East Street, Dongyang, Zhejiang, 322100, China ' School of Economics and Management, Shanghai Zhongqiao Vocational and Technical University, No. 3888, Caolanggong Road, Jinshan District, Shanghai, 201514, China ' Logistics Management Department, Zhejiang Tobacco Monopoly Administration (Company), No. 9, General Road, Shangcheng District, Hangzhou, Zhejiang, 310001, China ' Entrepreneurship School, Zhejiang Guangsha Vocational and Technical University of Construction, No. 1, Guangfu East Street, Dongyang, Zhejiang, 322100, China

Abstract: On the basis of fully analysing the many problems of manual scheduling operations in traditional cigarette storage and transportation enterprises, the industry's internal and external transportation resources are integrated through standardised, digital, and intelligent transportation management mechanisms. At the same time, a vehicle loading standardisation rule system is set based on the product characteristics of cigarette logistics and transportation market standards. For the first time, a digital model of cigarette product loading for freight vehicles with cost efficiency and low-carbon logistics is creatively constructed. Finally, a genetic algorithm based loading problem simulation is implemented through Python programming. Simulation results show that the proposed intelligent loading algorithm can automatically generate optimal loading plans, sharply reduce manual scheduling effort, shorten order-response time, and improve the turnover efficiency of both vehicles and goods, thereby enhancing overall operational performance and internal management of cigarette logistics enterprises.

Keywords: intelligent cigarette loading; cost optimisation; low-carbon logistics; genetic algorithm; capacity digitalisation; numerical simulation.

DOI: 10.1504/IJCSM.2026.154301

International Journal of Computing Science and Mathematics, 2026 Vol.23 No.2, pp.134 - 146

Received: 20 Oct 2025
Accepted: 12 Feb 2026
Published online: 19 Jun 2026 *

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