Real-time ambulance relocation: Assessing real-time redeployment strategies for ambulance relocation

Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight into how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real dataset from ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0–1 performance criterion for assessing the fraction of late arrivals only differs slightly from related response time criteria for evaluating the performance as a function of the response time, (2) adding more relocation decision moments is highly beneficial, particularly for rural areas, (3) considering ambulances involved in dropping off patients available for newly coming incidents reduces relocation times only slightly, and (4) simulation experiments for assessing move-up policies are highly preferable to simple mathematical models.

[1]  Peter J. Kolesar,et al.  An Algorithm for the Dynamic Relocation of Fire Companies , 1974, Oper. Res..

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

[3]  Erhan Erkut,et al.  Ambulance location for maximum survival , 2008 .

[4]  Matthew S. Maxwell,et al.  Approximate Dynamic Programming for Ambulance Redeployment , 2010, INFORMS J. Comput..

[5]  Karl F. Doerner,et al.  Ambulance location and relocation problems with time-dependent travel times , 2010, Eur. J. Oper. Res..

[6]  John J. Bernardo,et al.  Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky , 1994 .

[7]  Samir Elhedhli,et al.  A stochastic optimization model for real-time ambulance redeployment , 2013, Comput. Oper. Res..

[8]  Gilbert Laporte,et al.  Ambulance location and relocation models , 2000, Eur. J. Oper. Res..

[9]  Lei Zhang,et al.  Simulation Optiisation and Markov Models for Dynamic Ambulance Redeployment , 2012 .

[10]  Maria E. Mayorga,et al.  A nested-compliance table policy for emergency medical service systems under relocation , 2016 .

[11]  Cem Saydam,et al.  A multiperiod set covering location model for dynamic redeployment of ambulances , 2008, Comput. Oper. Res..

[12]  Jan L Jensen,et al.  Offload zones to mitigate emergency medical services (EMS) offload delay in the emergency department: a process map and hazard analysis. , 2015, CJEM.

[13]  Oded Berman,et al.  Dynamic Repositioning of Indistinguishable Service Units on Transportation Networks , 1981 .

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

[15]  Pieter L. van den Berg,et al.  Time-dependent MEXCLP with start-up and relocation cost , 2015, Eur. J. Oper. Res..

[16]  Lara Wiesche,et al.  Time-dependent ambulance allocation considering data-driven empirically required coverage , 2015, Health care management science.

[17]  Thije van Barneveld,et al.  The Minimum Expected Penalty Relocation Problem for the Computation of Compliance Tables for Ambulance Vehicles , 2016, INFORMS J. Comput..

[18]  S. Bhulai,et al.  A dynamic ambulance management model for rural areas , 2017, Health care management science.

[19]  Sandjai Bhulai,et al.  Compliance tables for an EMS system with two types of medical response units , 2017, Comput. Oper. Res..

[20]  Sandjai Bhulai,et al.  An efficient heuristic for real-time ambulance redeployment , 2015 .

[21]  Michel Gendreau,et al.  The maximal expected coverage relocation problem for emergency vehicles , 2006, J. Oper. Res. Soc..

[22]  Oded Berman,et al.  Repositioning of distinguishable urban service units on networks , 1981, Comput. Oper. Res..

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

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

[25]  Sandjai Bhulai,et al.  Ambulance Dispatch Center Pilots Proactive Relocation Policies to Enhance Effectiveness , 2018, Interfaces.

[26]  Vtv,et al.  Referentiekader spreiding en beschikbaarheid ambulancezorg 2008 , 2009 .

[27]  Tobias Andersson Granberg,et al.  Decision support tools for ambulance dispatch and relocation , 2007, J. Oper. Res. Soc..

[28]  Angel Ruiz,et al.  Recent Advances in Emergency Medical Services Management , 2015 .

[29]  Sandjai Bhulai,et al.  The effect of ambulance relocations on the performance of ambulance service providers , 2016, Eur. J. Oper. Res..

[30]  Verena Schmid,et al.  Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming , 2012, Eur. J. Oper. Res..

[31]  Armann Ingolfsson,et al.  A Markov Chain Model for an EMS System with Repositioning , 2013 .

[32]  Michel Gendreau,et al.  A dynamic model and parallel tabu search heuristic for real-time ambulance relocation , 2001, Parallel Comput..

[33]  Mohammad Mehdi Sepehri,et al.  Two new models for redeployment of ambulances , 2014, Comput. Ind. Eng..