A matheuristic approach for finding effective base locations and team configurations for north west air ambulance (original) (raw)
Related papers
Comparing population and incident data for optimal air ambulance base locations in Norway
Scandinavian journal of trauma, resuscitation and emergency medicine, 2018
Helicopter emergency medical services are important in many health care systems. Norway has a nationwide physician manned air ambulance service servicing a country with large geographical variations in population density and incident frequencies. The aim of the study was to compare optimal air ambulance base locations using both population and incident data. We used municipality population and incident data for Norway from 2015. The 428 municipalities had a median (5-95 percentile) of 4675 (940-36,264) inhabitants and 10 (2-38) incidents. Optimal helicopter base locations were estimated using the Maximal Covering Location Problem (MCLP) optimization model, exploring the number and location of bases needed to cover various fractions of the population for time thresholds 30 and 45 min, in green field scenarios and conditioned on the existing base structure. The existing bases covered 96.90% of the population and 91.86% of the incidents for time threshold 45 min. Correlation between mu...
Strategic ambulance location for heterogeneous regions
European Journal of Operational Research, 2017
Providing Emergency Medical Services (EMS) is a key function of society. To achieve high quality EMS, planning is of vital importance. An important strategic and tactical problem is the location of ambulance stations and the allocation of ambulances to these stations. This paper presents a new mixed integer model for this problem especially suitable for regions with heterogeneous demand and multiple performance measures. The model decides on the location/allocation of stations/ambulances, calculates the service and arrival rates for each station and the probabilities that a call is served by a particular station. The model is tested on a combined urban and rural area in Norway with multiple performance measures. Compared with the current solution for the area, the best solution from the model has a higher expected performance on each of the performance measures used.
Introducing fairness in Norwegian air ambulance base location planning
Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine
Background A primary task of the Norwegian helicopter emergency medical services (HEMS) is to provide advanced medical care to the critical ill and injured outside of hospitals. Where HEMS bases are located, directly influences who in the population can be reached within a given response time threshold and who cannot. When studying the locations of bases, the focus is often on efficiency, that is, maximizing the total number of people that can be reached within a given set time. This approach is known to benefit people living in densely populated areas, such as cities, over people living in remote areas. The most efficient solution is thus typically not necessarily a fair one. This study aims to incorporate fairness in finding optimal air ambulance base locations. Methods We solve multiple advanced mathematical optimization models to determine optimal helicopter base locations, with different optimization criteria related to the level of aversion to inequality, including the utilita...
An optimization model for locating and sizing emergency medical service stations
Journal of medical systems, 2010
Emergency medical services (EMS) play a crucial role in the overall quality and performance of health services. The performance of these systems heavily depends on operational success of emergency services in which emergency vehicles, medical personnel and supporting equipment and facilities are the main resources. Optimally locating and sizing of such services is an important task to enhance the responsiveness and the utilization of limited resources. In this study, an integer optimization model is presented to decide locations and types of service stations, regions covered by these stations under service constraints in order to minimize the total cost of the overall system. The model can produce optimal solutions within a reasonable time for large cities having up to 130 districts or regions. This model is tested for the EMS system of Adana metropolitan area in Turkey. Case study and computational findings of the model are discussed in detail in the paper.
A simulation-based iterative method for a trauma center: air ambulance location problem
2012
Timely transport of a patient to a capable medical facility is a key factor in providing quality care for trauma patients. This paper presents a mathematical model and a related solution method to search for optimal locations of trauma centers and air ambulances. The complicatedness of this problem stems from the characteristic that optimal locations for the two resources are coupled with each other. Specifically, this coupling makes it difficult to develop a priori estimates for the air ambulance's busy fraction, which are required to construct a probabilistic location model. We propose a method that uses integer programming and simulation to iteratively update busy fraction parameters in the model. Experimental results show that the proposed method is valid and improves the solution quality compared to alternative methods. We use real data on Korean trauma cases, and apply the method to the design of a trauma care system in Korea.
Providing a model for the issue of multi-period ambulance location
International Journal of Innovation in Engineering (IJIE), 2021
In this study, two mathematical models have been developed for assigning emergency vehicles, namely ambulances, to geographical areas. The first model, which is based on the assignment problem, the ambulance transfer (moving ambulances) between locations has not been considered. As ambulance transfer can improve system efficiency by decreasing the response time as well as operational cost, we consider this in the second model, which is based on the transportation problem. Both models assume that the demand of all geographical locations must be met. The major contributions of this study are: ambulance transfer between locations, day split into several time slots, and demand distribution of the geographical zone. To the best of our knowledge the first two have not been studied before. These extensions allow us to have a more realistic model of the real-world operation. Although, in previous studies, maximizing coverage has been the main objective of the goal, here, minimizing operating costs is a function of the main objective, because we have assumed that the demand of all geographical areas must be met.
International Journal of Medical Informatics, 2019
To achieve high performing emergency medical services (EMS), planning is of vital importance. EMS planners face several challenges when managing ambulance stations and the fleet of ambulances. In this paper, three strategic cases for EMS planners are presented together with potential solutions. In the first case, the effects of closing down a local emergency room (ER) are analyzed together with how adding an ambulance station and an ambulance to the area affected by the closing of the ER can be used to mitigate the negative consequences from the closing. The second case investigates a change in the organization of EMS. Currently, many non-urgent transport assignments are performed by ambulances which make them unavailable for more urgent calls. The potential for a more effective utilization of the ambulances is explored through transferring these assignments to designated transport vehicles. The third case is more technical and challenges the common practice regarding how time dependent demand is handled. Looking at the busiest hour or the average daily demand, is compared with taking time varying demand into account. The cases and solutions are studied using a recently developed strategic ambulance station location and ambulance allocation model for the Maximum Expected Performance Location Problem with Heterogeneous Regions (MEPLP-HR). The model has been extended to also include multiple time periods. This article demonstrates an innovative use of the model and how it can be applied to find and evaluate solutions to real cases within the field of strategic planning of EMS. The model is found to be a useful decision support tool when analyzing the cases and the expected performance of potential solutions.
Omega-international Journal of Management Science, 1997
In the location of ambulance bases for medical assistance, an adequate time of response must be guaranteed for each area in the region covered, incurring the minimum operating costs. Several linear models (such as the maximal covering location problem, MCLP) have been developed for designing these emergency systems which guarantee a certain cover whilst minimising determined costs. The computational difficulty involved in resolving large scale problems occasionally means trying to offer solutions using metaheuristics. This article presents the solution to the problem of locating ambulance bases in the province of Le6n (Spain), using the tabu search metaheuristic, which in its simplest version already offers good results, and which makes it a tool to be kept very much in mind when a rapid solution is needed to such problems. © 1997 Elsevier Science Ltd
Health Systems
Since 1997, special paediatric intensive care retrieval teams (PICRTs) based in 11 locations across England and Wales have been used to transport sick children from district general hospitals to one of 24 paediatric intensive care units. We develop a location allocation optimisation framework to help inform decisions on the optimal number of locations for each PICRT, where those locations should be, which local hospital each location serves and how many teams should station each location. Our framework allows for stochastic journey times, differential weights for each journey leg and incorporates queuing theory by considering the time spent waiting for a PICRT to become available. We examine the average waiting time and the average time to bedside under different number of operational PICRT stations, different number of teams per station and different levels of demand. We show that consolidating the teams into fewer stations for higher availability leads to better performance.
A multiperiod set covering location model for dynamic redeployment of ambulances
Computers & Operations Research, 2008
Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided. ᭧