A taxonomy for emergency service station location problem

The emergency service station (ESS) location problem has been widely studied in the literature since 1970s. There has been a growing interest in the subject especially after 1990s. Various models with different objective functions and constraints have been proposed in the academic literature and efficient solution techniques have been developed to provide good solutions in reasonable times. However, there is not any study that systematically classifies different problem types and methodologies to address them. This paper presents a taxonomic framework for the ESS location problem using an operations research perspective. In this framework, we basically consider the type of the emergency, the objective function, constraints, model assumptions, modeling, and solution techniques. We also analyze a variety of papers related to the literature in order to demonstrate the effectiveness of the taxonomy and to get insights for possible research directions

Ambulance Emergency Response Optimization in Developing Countries

The lack of emergency medical transportation is viewed as the main barrier to the access of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We traveled to Dhaka, Bangladesh, the sixth largest and third most densely populated city in the world, to conduct field research resulting in the collection of two unique datasets that inform our approach. This data is leveraged to develop machine learning methodologies to estimate demand for emergency medical services in a LMIC setting and to predict the travel time between any two locations in the road network for different times of day and days of the week. We combine our robust optimization and machine learning frameworks with real data to provide an in-depth investigation into three policy-related questions. First, we demonstrate that outpost locations optimized for weekday rush hour lead to good performance for all times of day and days of the week. Second, we find that significant improvements in emergency response times can be achieved by re-locating a small number of outposts and that the performance of the current system could be replicated using only 30% of the resources. Lastly, we show that a fleet of small motorcycle-based ambulances has the potential to significantly outperform traditional ambulance vans. In particular, they are able to capture three times more demand while reducing the median response time by 42% due to increased routing flexibility offered by nimble vehicles on a larger road network. Our results provide practical insights for emergency response optimization that can be leveraged by hospital-based and private ambulance providers in Dhaka and other urban centers in LMICs

A Chance Constrained Programming Model for Reliable Emergency Vehicles Relocation Problem

AbstractEmergency vehicles relocation is one mechanism of increasing preparedness for potential emergencies. This paper addresses the problem of designing reliable emergency vehicles relocation system. Under this respect, we extend the DYNACO model with chance-constrained programming framework for the optimal redeployment of emergency vehicles. The model deals with the availability of emergency vehicles by approximate hypercube. In addition, other random elements including travel time and emergency demand are taken into account in the model. Solution procedure based on genetic algorithm and Monte-Carlo simulation is developed to solve the stochastic model. Computational experiences are reported to illustrate the performance and the effectiveness of the proposed solution

The Minimum Expected Penalty Relocation Problem for the computation of compliance tables for ambulance vehicles

We study the ambulance relocation problem in which one tries to respond to possible future incidents quickly. For this purpose, we consider compliance table policies: a relocation strategy commonly used in practice. Each compliance table level indicates the desired waiting site locations for the available ambulances. To compute efficient compliance tables, we introduce the minimum expected penalty relocation problem (MEXPREP), which we formulate as an integer linear program. In this problem, one has the ability to control the number of waiting site relocations. Moreover, different performance measures related to response times, such as survival probabilities, can be incorporated. We show by simulation that the MEXPREP compliance tables outperform both the static policy and compliance tables obtained by the maximal expected coverage relocation problem (MECRP), which both serve as benchmarks. Besides, we perform a study on different relocation thresholds and on two different methods to assign available ambulances to desired waiting sites

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

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201

Optimization approaches to the ambulance dispatching and relocation problem

In the Emergency Medical Service (EMS) context, the decision-making process plays a very important role since some decisions highly impact patients’ health. This thesis focuses on the operational level by solving the dispatching and relocation ambulance problems. Dispatching decisions assign ambulances to emergencies, and the relocation problem decides to which base ambulances should be (re)assigned. Two optimization approaches are proposed to improve the effectiveness and efficiency in the EMS response: a mixed-integer linear programming (MILP) model and a pilot method heuristic. The aim is to maximize the system’s coverage using a time-preparedness measure allowing relocations to any base. Experiments are performed using EMS data from Lisbon, Portugal, where solving these problems is still a handmade task. Different ambulance types are considered, which should be used according to the severity of each emergency. The proposed approaches are tested under different scenarios: varying the period size, varying the number of emergencies, and simulating a whole day. Furthermore, they are adapted to compare the proposed strategy with the current Portuguese EMS strategy, which dispatches the closest available ambulance for each emergency and always relocates ambulances to their home bases. Results highlight the potential of the mathematical model and of the proposed strategy to be applied in realtime contexts since a reduction of 10% is obtained in the average response time to emergencies in the simulation scenario. The heuristic should be used when more emergencies occur in the same time period since a solution can be obtained almost immediately in contrast to the MILP usage. To help EMS managers in the decision-making process, we propose an ambulance management tool using Geographic Information Systems, which embeds the proposed approaches. It can be used in real-time or for simulation purposes. It incorporates a map visualization that analyzes ambulances’ movements on the map and the emergencies’ location

A mathematical programming approach for dispatching and relocating EMS vehicles.

We consider the problem of dispatching and relocating EMS vehicles during a pandemic outbreak. In such a situation, the demand for EMS vehicles increases and in order to better utilize their capacity, the idea of serving more than one patient by an ambulance is introduced. Vehicles transporting high priority patients cannot serve any other patient, but those transporting low priority patients are allowed to be rerouted to serve a second patient. We have considered three separate problems in this research. In the first problem, an integrated model is developed for dispatching and relocating EMS vehicles, where dispatchers determine hospitals for patients. The second problem considers just relocating EMS vehicles. In the third problem only dispatching decisions are made where hospitals are pre-specified by patients not by dispatchers. In the first problem, the objective is to minimize the total travel distance and the penalty of not meeting specific constraints. In order to better utilize the capacity of ambulances, we allow each ambulance to serve a maximum of two patients. Considerations are given to features such as meeting the required response time window for patients, batching non-critical and critical patients when necessary, ensuring balanced coverage for all census tracts. Three models are proposed- two of them are linear integer programing and the other is a non-linear programing model. Numerical examples show that the linear models can be solved using general-purpose solvers efficiently for large sized problems, and thus it is suitable for use in a real time decision support system. In the second problem, the goal is to maximize the coverage for serving future calls in a required time window. A linear programming model is developed for this problem. The objective is to maximize the number of census tracts with single and double coverage, (each with their own weights) and to minimize the travel time for relocating. In order to tune the parameters in this objective function, an event based simulation model is developed to study the movement of vehicles and incidents (911 calls) through a city. The results show that the proposed model can effectively increase the system-wide coverage by EMS vehicles even if we assume that vehicles cannot respond to any incidents while traveling between stations. In addition, the results suggest that the proposed model outperforms one of the well-known real time repositioning models (Gendreau et al. (2001)). In the third problem, the objective is to minimize the total travel distance experienced by all EMS vehicles, while satisfying two types of time window constraints. One requires the EMS vehicle to arrive at the patients\u27 scene within a pre-specified time, the other requires the EMS vehicle to transport patients to their hospitals within a given time window. Similar to the first problem, each vehicle can transport maximum two patients. A mixed integer program (MIP) model is developed for the EMS dispatching problem. The problem is proved to be NP-hard, and a simulated annealing (SA) method is developed for its efficient solution. Additionally, to obtain lower bound, a column generation method is developed. Our numerical results show that the proposed SA provides high quality solutions whose objective is close to the obtained lower bound with much less CPU time. Thus, the SA method is suitable for implementation in a real-time decision support system