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Multi-objective optimization of the route problem for hazardous goods transportation

Published: 03 July 2024 Publication History

Abstract

To ensure the safe transportation of hazardous goods while minimizing transportation costs, a multi-objective optimization method is adopted to determine the optimal driving path of hazardous goods transportation vehicles. By simplifying the quantification process of risk factors for transportation vehicles passing through densely populated areas, with the goal of minimizing the number of vehicles, the total transportation distance, and the shortest driving distance through densely populated areas, an optimization model for the transportation path problem of hazardous goods vehicles is established. A multi-objective evolutionary algorithm based on probability model is designed for the model. Taking a hazardous material transportation network with 5 transportation vehicles, 24 demand nodes, and 6 densely populated areas as an example, validate the model and algorithm. The example results show that the Pareto solution of the vehicle routing problem can be obtained using this algorithm, providing decision support for the scheduling of hazardous goods transportation vehicles.

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    GAIIS '24: Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
    May 2024
    439 pages
    ISBN:9798400709562
    DOI:10.1145/3665348
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 July 2024

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