Work

Integrating consumer behavior into route optimization: A SEM-informed genetic algorithm for sustainable last-mile logistics

Year: 2026

Type: article

Abstract: The paper addresses the urgent problem of reconciling environmental sustainability with operational efficiency in current logistics by presenting a new AI-based framework that directly considers the complexities of consumer behavior among Gen Z/Millennial Chinese consumers. Since environmental concern increases the demand for green logistics, classical optimization models do not tend to reflect the peculiarities of consumer preferences and the new confidence in AI-based solutions. This methodology is distinguished by a perfect combination of Structural Equation Modelling (SEM) to empirically derive the findings of consumer behavior, and a Genetic Algorithm (GA)-based optimization model designed to meet the needs of logistics. This analysis is based on the primary consumer data collected by WeChat from students and young professionals in Chinese universities. The integrated model also takes into account real-life constraints like vehicle capacity, demand variability, and time windows. The SEM analysis indicated that the perceived utility (β =0.64, p < 0.01) and environmental awareness (β =0.76, p < 0.01) have the greatest influence on the adoption of green transportation, with younger customers (ages 18–24, 88.34%) being more supportive of it. However, building trust in AI-driven logistics is still a challenge (β =0.39, p < 0.05). These behavioral observations (as developed using SEM) are incorporated systematically as explicit constraints and elements of the objective function (Behavioral Penalty term) in the GA-based optimization framework. This model reduces emissions and consumer-preference penalties while achieving a small reduction in total generalized cost, although pure operational cost increases. Our results reveal the disruptive potential of AI in terms of efficiency and sustainability in supply chains and support policy incentives, and further research on advanced behavioral analytics that can contribute to consumer trust and facilitate the widespread adoption of these technologies.

Source: Research in Transportation Business & Management

DOI: 10.1016/j.rtbm.2026.101853

Authors: Furqan Sikandar, Ahmed Naser Alzurfi, Mohanad R. Aljanabi, Lu Hanggao, Jiang Sujian, Khandakar Ahmed

Institutions: Zhejiang Guangsha Vocational and Technical University of Construction, University of Kufa, Victoria University

Language: English

Topic: Urban and Freight Transport Logistics

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Responsible consumption and production, Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $2,620