Large-scale emergencies occur frequently around the world, causing serious casualties. Emergency evacuation is one of the top priorities after a disaster. Due to the uncertain and complex evacuation processes, evacuation plans obtained in a deterministic context may not meet the requirements of practical engineering applications. This research considers a multi-crowd congestion-relieved evacuation problem in Gaussian Type-2 fuzzy environments. A path-based one destination network flow (P-ODNF) model is developed for the problem formulation. To relieve the traffic congestion, a multi-point diversion evacuation strategy is employed in the model’s construction. The uncertainties associated with the evacuation process are expressed as Gaussian Type-2 fuzzy variables. A critical value-based defuzzification technique is adopted to handle the Type-2 fuzziness. Based on the credibility measure, the uncertain P-ODNF model is transformed into its deterministic counterpart. An efficient adaptive chaos particle swarm optimization algorithm (A-CPSO) is designed for model solving. Several numerical experiments are performed to demonstrate the proposed methodology. A sensitivity analysis is performed to illustrate the logical correctness of the proposed defuzzification process. The computational results show that A-CPSO is competitive in both its effectiveness and efficiency. The proposed methodology can coordinate multiple simultaneous evacuation processes to relieve congestion and improves the overall evacuation efficiency.


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    Titel :

    A Gaussian Type-2 Fuzzy Programming Approach for Multicrowd Congestion-Relieved Evacuation Planning


    Beteiligte:
    Men, Jinkun (Autor:in) / Chen, Guohua (Autor:in) / Chen, Peizhu (Autor:in) / Zhou, Lixing (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2022-11-01


    Format / Umfang :

    2199010 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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