Where to place emergency ambulance vehicles: use of a capacitated maximum covering location model with real call data

Abstract

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents

“…In the location-allocation research area of medical support vehicles (ambulances), Zaffar and colleagues [7] found that the main objective of most state-of-the-art work is related to finding the best distribution of ambulances to maximize coverage and minimize response times. Mohri and Haghshenas [8] analyzed the problem using the Ambulance Location Problem (ALP) as an extension of the Set Coverage Problem introduced by Toregas [9]; Kavhe and Mesgari combined the Maximum Coverage Location Problem (MCLP) and metaheuristics [10]; Hashtarkhani et al proposed the use of MCLP and combinatorial optimization techniques [11]; while Barojas-Payán et al [12] addressed the problem using the p-median problem.…”

“…Moreover, there is no known efficient algorithm that can solve all instances of the problem in polynomial time in an NP-hard problem. To reduce computational complexity, some proposals require the list of possible candidate locations in advance or otherwise include in the methodology an approach to decrease the search space [11,21]. Saeidian et al [18] used a GIS to select candidate locations that satisfied the initial conditions, followed by applying a metaheuristic to calculate the optimal location of temporary help centers.…”

“…To have a tolerance in the capacity of each base, half of the total capacity is taken as a value. Equation (11) describes the calculation of demand (d j ) for a given demand point (p j ).…”

“…The value of 9 min is a standard recommended by [47] and is used in the models described in [7,48]. However, this is an ideal case, so they propose the range [9][10][11][12][13] min. Our assumption is that there is at least one ambulance on standby in each location, which is dispatched the moment a victim is confirmed, and another unit is immediately requested from the corresponding base, even if no other victim is confirmed.…”

Enhancing Health Care Accessibility and Equity Through a Geoprocessing Toolbox for Spatial Accessibility Analysis: Development and Case Study

“…Supply relates to the infrastructure’s locations (eg, health care providers); demand refers to the locations of individuals who are expected to use the infrastructure (eg, patients); and mobility considers the travel costs between demand and supply locations (eg, driving time) [ 10 ]. Identifying areas with limited spatial accessibility enables planners and policy makers to understand the distribution of health service locations and reveal and address spatial inequities [ 11 ].…”