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

STRATEGIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS UNDER MORE REALISTIC CONDITIONS

Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems

DISTRICTING AND DISPATCHING POLICIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS

The major focus of Emergency Medical Service (EMS) systems is to save lives and to minimize the effects of emergency health incidents. The efficiency of the EMS systems is a major public concern. Thus, over the past three decades a significant amount of research studies have been conducted to improve the performance of EMS systems. The purpose of this study is also to improve the performance of EMS system. The contribution of this research towards improving the performance of EMS systems is twofold. One area is to implement optimal or near optimal dispatching strategies for EMS systems and the other is to determine the response boundaries for EMS vehicles. Proposed dispatching strategies are implemented incorporating the degree of the urgency of the call. A Markov decision process (MDP) model is developed to obtain optimal dispatching strategies in less complex models. A heuristic algorithm is proposed to dispatch ambulances for more complex models. In this study, an integer programming formulation and a constructive heuristic are proposed to determine response areas or districts for each ambulance. Additionally, dispatching rules to dispatch paramedic units within districts and out of districts are examined. Simulation is used to evaluate the performance of the EMS system after introducing proposed dispatching policies. Performance is measured in terms of patients\u27 survival probability rather than measuring the response time thresholds, since survival probability reflects the patients\u27 outcome directly. Results are illustrated using real-data collected from Hanover county Virginia. Results show that proposed dispatching rules are valuable in increasing patients\u27 survivabilit

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

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

FOCUSING ON CENTRALITY MEASURE IN EMERGENCY MEDICAL SERVICES

Emergency Medical Services (EMS) attracted many researchers because the demand of EMS was increasing over time. One of the major concerns of EMS is the response time and ambulance despatching is one of the vital factors which affects the response time. This paper focuses on the problem of ambulance despatching when many emergency calls emerge in a short time, which exists under the condition of catastrophic natural or manmade disasters. We modify a new method for ambulance despatching by centrality measure, this method constructs a nearest-neighbor coupled emergency call network and then prioritize those calls by the score of fitness, where the score of fitness considers two factors: centralized measure a call by the emergency call network and the closest policy which means despatching to the closest call site. This method is testified by a series of simulation experiments on the real topology road network of Hong Kong Island which contains 8 hospitals. These analyses demonstrate the real situation and proof the potential of centrality measure in reducing response time of EMS