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The transportation of hazardous materials (HazMat) plays a crucial role for satisfying the industrial and the customer demand in our modern society. Given the dangerous nature of these materials, which can pose fatal risks to human health and the environment, efficient management of HazMat transportation requires advanced decision-making tools to address safety, cost, and operational constraints. To this end, this study introduces a Decision Support System for a bi-objective vehicle routing problem. It combines two well-known variants: the Vehicle Routing Problem with Time Windows (VRPTW) and the Hazardous Materials Vehicle Routing Problem (HazMat VRP). The new variant is named BHMVRPTW (Bi-objective Hazardous Materials Vehicle Routing Problem with Time Windows) aiming to minimize the total transportation cost and the travel risk. Considering the \(\mathcal {N}\mathcal {P}\) -hardness of the problem, a Genetic Algorithm is introduced as a solution approach. The algorithm is tested on the Solomon benchmark instances for VRPTW. The results demonstrate that the proposed method is highly competitive and effective for the multi-objective optimization of vehicle routing problems.

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An Evolutionary Algorithm-Based Decision Support System for Bi-objective Hazardous Materials Transportation

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  • 发表日期：

    2026年

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  • Nasreddine Ouertani,
  • Saoussen Krichen

摘要

The transportation of hazardous materials (HazMat) plays a crucial role for satisfying the industrial and the customer demand in our modern society. Given the dangerous nature of these materials, which can pose fatal risks to human health and the environment, efficient management of HazMat transportation requires advanced decision-making tools to address safety, cost, and operational constraints. To this end, this study introduces a Decision Support System for a bi-objective vehicle routing problem. It combines two well-known variants: the Vehicle Routing Problem with Time Windows (VRPTW) and the Hazardous Materials Vehicle Routing Problem (HazMat VRP). The new variant is named BHMVRPTW (Bi-objective Hazardous Materials Vehicle Routing Problem with Time Windows) aiming to minimize the total transportation cost and the travel risk. Considering the \(\mathcal {N}\mathcal {P}\) -hardness of the problem, a Genetic Algorithm is introduced as a solution approach. The algorithm is tested on the Solomon benchmark instances for VRPTW. The results demonstrate that the proposed method is highly competitive and effective for the multi-objective optimization of vehicle routing problems.