A dynamic ambulance management model for rural areas

Barneveld, van Thije; Bhulai, Sandjai; Mei, van der Rob

doi:10.1007/s10729-015-9341-3

Cited by 32 publications

(23 citation statements)

References 18 publications

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“…Such models include the dynamic extensions of DSM (Gendreau, Laporte, & Semet, 2001) and MEXCLP (Gendreau, Laporte, & Semet, 2006;Van Barneveld, 2016). Additionally, recent approaches addressed the problem using heuristics (Jagtenberg, Bhulai, & Van der Mei, 2015;Van Barneveld, Bhulai, & Van der Mei, 2015), approximate dynamic programming (Maxwell, Restrepo, Henderson, & Topaloglu, 2010;Schmid, 2012), stochastic optimization (Naoum-Sawaya & Elhedhli, 2013), and Markov chains (Alanis, Ingolfsson, & Kolfal, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Fire truck relocation during major incidents

Usanov

Legemaate²,

Ven

et al. 2019

Naval Research Logistics

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The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam-Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance.

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Section: Literature Reviewmentioning

confidence: 99%

Fire truck relocation during major incidents

Usanov

Legemaate²,

Ven

et al. 2019

Naval Research Logistics

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“…This article proposes a policy that includes both the ambulance dispatch as well as relocating idle ambulances, and uses a fast tree-search heuristic to solve DYNAROC. A one-step look-ahead heuristic is the considered in [26]. Several scenarios are constructed that may occur one time-step later and these scenarios are combined with each possible relocation decision to obtain a classification of these possible decisions.…”

Section: Related Workmentioning

confidence: 99%

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

Barneveld

Jagtenberg

Bhulai

et al. 2018

Socio-Economic Planning Sciences

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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 in 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-life dataset of 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 late arrivals only differs slightly from taking expert-opinion based S-curve for evaluating *

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“…At the operational level dynamic ambulance management (DAM) is widely used [15,19,35,38,33,34,42,2,24,46,47]. Sometimes simulation tools are used to validate models.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand-point constrained EMS vehicle allocation problems for regions with both urban and rural areas

Buuren

Mei

Bhulai

2018

Operations Research for Health Care

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Governments deal with increasing health care demand and costs, while budgets are tightened. At the same time, ambulance providers are expected to deliver high-quality service at affordable cost. Maximum reliability and minimal availability models guarantee a minimal performance level at each demand point, in contrast to the majority of facility location and allocation methods that guarantee a minimal performance that is aggregated over the entire ambulance region. As a consequence, existing models generally lead to overstaffing, particularly in 'mixed' regions with both urban and rural areas, which leads to unnecessarily high costs. This paper addresses this problem. First, we introduce the concept of demand projection to give fundamental insight into why this overstaffing takes place. Next, we overcome the overstaffing by the so-called adjusted queuing (AQ) solution that provides generalizations of the existing models. We provide mathematical proofs for the correctness of the AQ solution. Finally, to assess the performance of the AQsolution we have performed extensive numerical experimentation, using real data from four ambulance regions in the Netherlands. The results show that in all cases the AQ-solution indeed leads to better ambulance care than the existing solutions, while reducing staffing cost.

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A dynamic ambulance management model for rural areas

Cited by 32 publications

References 18 publications

Fire truck relocation during major incidents

Fire truck relocation during major incidents

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

Demand-point constrained EMS vehicle allocation problems for regions with both urban and rural areas

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