Data-driven strategic optimization for sustainable electric last-mile delivery
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I presented our research on data-driven strategic optimization approaches for sustainable electric last-mile delivery at the XVI Congreso de Ingeniería del Transporte (CIT 2025) in Zaragoza. This work applies the strategic decision-making framework from my industrial PhD research to the specific challenges of urban logistics and electric vehicle fleet management.
The presentation addressed how small and medium-sized logistics companies can adopt optimization techniques for electric last-mile delivery operations despite facing resource constraints, limited technical expertise, and high computational costs. Our work began with a comprehensive literature review examining how heuristics, metaheuristics, and machine learning approaches support multi-objective optimization in this context—balancing delivery efficiency, sustainability goals, vehicle range constraints, and operational costs. We then presented a practical case study demonstrating the application of these techniques in real SME logistics operations.
A key focus was demonstrating actionable frameworks that SMEs in the logistics sector can implement without requiring extensive AI infrastructure or specialized data science teams. The work shows how data-driven approaches strengthen operational resilience and strategic planning in urban delivery while contributing to environmental sustainability through optimized electric vehicle routing and intelligent charging strategies. We provide a roadmap for smaller logistics operators to transition toward greener, more efficient delivery operations in our literature review article.
The session generated productive discussions about adoption barriers faced by logistics SMEs and the potential for hybrid optimization methods to make these technologies accessible to smaller operators in the transport sector, ultimately supporting both economic competitiveness and sustainability objectives.
Related publication: Strategic Decision-Making in SMEs: A Review of Heuristics and Machine Learning for Multi-Objective Optimization