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<p>This paper presents a novel multi-criteria routing optimization approach specifically designed for large-scale urban transportation systems. It aims to simultaneously optimize multiple competing objectives including meteorological conditions, traffic density, energy consumption and travel time. Meteorological conditions consist of the following measures: temperature, humidity, atmospheric pressure, visibility, wind speed, and precipitation. Our approach leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate optimized routes that balance above objective functions in realistic urban environments. We demonstrate the effectiveness of our approach through extensive experiments on the realistic Munich’s urban road network, containing 200,477 nodes and 403,711 edges. Experimental results validate the scalability and practical applicability of our Weather-Aware approach in realistic urban transportation systems, by optimally achieving a trade-off between competing operational objectives under diverse meteorological conditions.</p>

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Comprehensive evolutionary multi-objective vehicle routing for holistic smart mobility

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Mohamed Amine Marzouk,
  • Ali El Kamel,
  • Habib Youssef

摘要

This paper presents a novel multi-criteria routing optimization approach specifically designed for large-scale urban transportation systems. It aims to simultaneously optimize multiple competing objectives including meteorological conditions, traffic density, energy consumption and travel time. Meteorological conditions consist of the following measures: temperature, humidity, atmospheric pressure, visibility, wind speed, and precipitation. Our approach leverages the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate optimized routes that balance above objective functions in realistic urban environments. We demonstrate the effectiveness of our approach through extensive experiments on the realistic Munich’s urban road network, containing 200,477 nodes and 403,711 edges. Experimental results validate the scalability and practical applicability of our Weather-Aware approach in realistic urban transportation systems, by optimally achieving a trade-off between competing operational objectives under diverse meteorological conditions.