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Iterative optimization algorithm with parameter estimation for the ambulance location problem

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Abstract

The emergency vehicle location problem to determine the number of ambulance vehicles and their locations satisfying a required reliability level is investigated in this study. This is a complex nonlinear issue involving critical decision making that has inherent stochastic characteristics. This paper studies an iterative optimization algorithm with parameter estimation to solve the emergency vehicle location problem. In the suggested algorithm, a linear model determines the locations of ambulances, while a hypercube simulation is used to estimate and provide parameters regarding ambulance locations. First, we suggest an iterative hypercube optimization algorithm in which interaction parameters and rules for the hypercube and optimization are identified. The interaction rules employed in this study enable our algorithm to always find the locations of ambulances satisfying the reliability requirement. We also propose an iterative simulation optimization algorithm in which the hypercube method is replaced by a simulation, to achieve computational efficiency. The computational experiments show that the iterative simulation optimization algorithm performs equivalently to the iterative hypercube optimization. The suggested algorithms are found to outperform existing algorithms suggested in the literature.

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Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2014R1A2A2A03003874)

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The authors declare there are no conflicts of interest in this work.

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Authors and Affiliations

  1. Department of information and Industrial Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 120-749, Korea

    Sun Hoon Kim & Young Hoon Lee

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  1. Sun Hoon Kim
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Correspondence to Sun Hoon Kim.

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Kim, S.H., Lee, Y.H. Iterative optimization algorithm with parameter estimation for the ambulance location problem. Health Care Manag Sci 19, 362–382 (2016). https://doi.org/10.1007/s10729-015-9332-4

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