Emergency service vehicle location problem with batch arrival of demands

In this paper an emergency service vehicle (ESV) location problem has been considered in which it is assumed that each emergency call may require more than one ESV. In ESV location problem two factors should be known; the location of stations and the number of ESVs at each station. Hence, a nonlinear mixed integer programming model is presented in order to maximize the total response rate to the emergency calls. Moreover, a solution method based on genetic algorithm is provided and efficiency of the algorithm is evaluated with regard to the results from an exhaustive enumeration method. The model is applied to the real case study based on the data from Mashhad city to find the emergency gas stations and the required ESVs. Finally, a sensitivity analysis on the main parameters of the model is conducted and the managerial insights were reported. The results indicate that considering the fact that each call may require more than one ESV is very influential on the response rate and the assumption of each call requires just one ESV makes the results unrealistic.

[1]  Charles S. ReVelle,et al.  The Maximum Availability Location Problem , 1989, Transp. Sci..

[2]  Reinaldo Morabito,et al.  A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways , 2007, Ann. Oper. Res..

[3]  Rajan Batta,et al.  The Maximal Expected Covering Location Problem: Revisited , 1989, Transp. Sci..

[4]  Gilbert Laporte,et al.  Solving an ambulance location model by tabu search , 1997 .

[5]  Cem Iyigun,et al.  Location analysis of emergency vehicles using an approximate queueing model , 2017 .

[6]  Xiaoyan Zhu,et al.  Covering models and optimization techniques for emergency response facility location and planning: a review , 2011, Math. Methods Oper. Res..

[7]  Charles S. ReVelle,et al.  The Location of Emergency Service Facilities , 1971, Oper. Res..

[8]  Reinaldo Morabito,et al.  Emergency service systems: The use of the hypercube queueing model in the solution of probabilistic location problems , 2008, Int. Trans. Oper. Res..

[9]  Reinaldo Morabito,et al.  Towards hypercube queuing models for dispatch policies with priority in queue and partial backup , 2017, Comput. Oper. Res..

[10]  Maria E. Mayorga,et al.  Joint location and dispatching decisions for Emergency Medical Services , 2013, Comput. Ind. Eng..

[11]  Bo Zhang,et al.  Covering location problem of emergency service facilities in an uncertain environment , 2017 .

[12]  Mark Goh,et al.  Covering problems in facility location: A review , 2012, Comput. Ind. Eng..

[13]  Reinaldo Morabito,et al.  Incorporating priorities for waiting customers in the hypercube queuing model with application to an emergency medical service system in Brazil , 2015, Eur. J. Oper. Res..

[14]  Angel B. Ruiz,et al.  Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles , 2019, Eur. J. Oper. Res..

[15]  Tonguç Ünlüyurt,et al.  Estimating the performance of emergency medical service location models via discrete event simulation , 2016, Comput. Ind. Eng..

[16]  Vladimir Marianov,et al.  The Queueing Maximal availability location problem: A model for the siting of emergency vehicles , 1996 .

[17]  Mark S. Daskin,et al.  A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution , 1983 .

[18]  Lan Mu,et al.  Modular Capacitated Maximal Covering Location Problem for the Optimal Siting of Emergency Vehicles , 2012 .

[19]  Richard C. Larson,et al.  Approximating the Performance of Urban Emergency Service Systems , 1975, Oper. Res..

[20]  Reinaldo Morabito,et al.  Towards unified formulations and extensions of two classical probabilistic location models , 2005, Comput. Oper. Res..

[21]  Laura A. McLay,et al.  A maximum expected covering location model with two types of servers , 2009 .