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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/115494

    Title: Demand Forecast and Multi-Objective Ambulance Allocation
    Authors: Tsia, Yihjia;Chang, Kuan-Wu;Yiang, Giou-Teng;Lin, Hwei Jen
    Keywords: Demand forecast;emergency medical services;qradtree decomposition;particle swarm optimization;ambulance allocation model
    Date: 2018-01-24
    Issue Date: 2018-11-01 12:10:55 (UTC+8)
    Publisher: World Scientific
    Abstract: This study considers the two-fold dynamic ambulance allocation problem, which includes
    forecasting the distribution of Emergency Medical Service (EMS) requesters and allocating
    ambulances dynamically according to the predicted distribution of requesters. EMSs demand
    distribution forecasting is based on on-record historical demands. Subsequently, a multi-objective
    ambulance allocation model (MOAAM) is solved by a mechanism called Jumping
    Particle Swarm Optimization (JPSO) according to the forecasted distribution of demands.
    Experiments were conducted using recorded historical data for EMS requesters in New Taipei
    City, Taiwan, for the years 2014 and 2015. EMS demand distribution for 2015 is forecasted
    according to the on-record historical demand of 2014. Ambulance allocation for 2015 is determined
    according to the anticipated demand distribution. The predicted demand distribution
    and ambulance allocation solved by JPSO are compared with historic data of 2015. The
    comparisons verify that the proposed methods yield lower forecasting error rates and better
    ambulance allocation than the actual one.
    Relation: International Journal of Pattern Recognition and Artificial Intelligence 32(7),p.1-21
    DOI: 10.1142/S0218001418590115
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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