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Electric vehicles

New Study on Electric Vehicle Routing Optimizes Logistics and Energy Distribution

Strategic Insights

— 1 min read

A recent study published in Applied Soft Computing has identified a new opportunity for electric vehicles (EVs) to enhance the efficiency of both transportation logistics and energy distribution. The research, conducted by a team of researchers from the University of Cambridge, has developed a multi-objective evolutionary algorithm that integrates the Vehicle Routing Problem with Time Windows (VRPTW) and Energy Transport (ET) to optimize energy logistics and reduce energy costs in urban mobility.

Key Takeaways:

  • The study aims to address the complex optimization challenges in integrating transportation logistics and energy distribution through a multi-objective evolutionary algorithm.
  • The proposed algorithm features constraint-aware initialization and problem-specific operators for routing, time windows, and energy logistics.
  • Experimental results on modified benchmarks showed that the integrated approach consistently outperformed decoupled baselines, achieving up to 30% reduction in energy costs and 20% fewer vehicles used.
  • The findings demonstrate the effectiveness of coordinated logistics-energy strategies in promoting cost-efficient and sustainable urban mobility.
  • The research has been peer-reviewed and is now available in Applied Soft Computing.
  • Authors Yue Xie, Kai-Fung Chu, Fumiya Iida, and Albert Y. S. Lam contributed to the study.
  • Financial support for the research came from the European Union (EU).
  • Keywords for this news article include: Cambridge, United Kingdom, Europe, Engineering, Algorithms, Evolutionary Algorithm, Mathematics, University of Cambridge.

Statistics:

  • Up to 30% reduction in energy costs achieved through the integrated approach.
  • 20% fewer vehicles used due to the optimized routing and energy logistics.
  • The study was supported by the European Union (EU).
  • The research has been published in Applied Soft Computing (Elsevier).
  • The citation for this news report is: NewsRx. Findings from University of Cambridge Reveals New Findings on Engineering (A Multi-objective Evolutionary Algorithm With Constraint-compliant Initialization for Energy Transport and Urban Logistics In Electric Vehicle Routing). Journal of Engineering. November 3, 2025; p 757.

Sources:

  • Applied Soft Computing, 2025; 183
  • Yue Xie, Kai-Fung Chu, Fumiya Iida, and Albert Y. S. Lam. A Multi-objective Evolutionary Algorithm With Constraint-compliant Initialization for Energy Transport and Urban Logistics In Electric Vehicle Routing. Applied Soft Computing. 2025; 183
  • European Union (EU)
  • University of Cambridge, Dept. of Engineering
  • Elsevier, Radarweg 29, 1043 Nx Amsterdam, Netherlands

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