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

Location models in the public sector

The past four decades have witnessed an explosive growth in the field of networkbased facility location modeling. This is not at all surprising since location policy is one of the most profitable areas of applied systems analysis in regional science and ample theoretical and applied challenges are offered. Location-allocation models seek the location of facilities and/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or several objectives generally related to the efficiency of the system or to the allocation of resources. This paper concerns the location of facilities or services in discrete space or networks, that are related to the public sector, such as emergency services (ambulances, fire stations, and police units), school systems and postal facilities. The paper is structured as follows: first, we will focus on public facility location models that use some type of coverage criterion, with special emphasis in emergency services. The second section will examine models based on the P-Median problem and some of the issues faced by planners when implementing this formulation in real world locational decisions. Finally, the last section will examine new trends in public sector facility location modeling.Location analysis, public facilities, covering models

IMPROVING QUALITY OF SERVICE IN EMS SYSTEMS BY REDUCING DISPARITIES BETWEEN SERVICE ZONES

Emergency medical service (EMS) systems respond to emergency or urgent calls so as to provide immediate care, such as pre-hospital care and/or transportation, to hospitals. Care must be provided in a timely manner; in fact quality of service is usually directly associated with response time. To reduce the response time, the number and location of vehicles within the service area are important variables. However with limited capacity, increasing the number of vehicles is often an infeasible alternative. Therefore, a critical design goal is to decide at which facilities stations should be located in order to serve as much demand as possible in a reasonable time, and at the same time maintain equitable service between customers. This study aims to focus on locating ambulances which respond to 911 calls in EMS systems. The goals are to find the optimal base station location for vehicles so that the number of calls or customers served is maximized while disparity between those customers is minimized, to consider the survival rate of patients directly in the model, and develop appropriate meta-heuristics for solving problems which cannot be solved optimally

Storage and Security of Supply in the Medium Run

This paper analyzes the role of private storage in a market for a commodity (e.g. natural gas) whose supply is subject to the threat of an irreversible disruption. We focus on the medium term in which seasonality of demand and exhaustibility can be neglected. We characterize the price and inventory dynamics (accumulation, drainage and limit stocks) in a competitive equilibrium with rational expectations. We show the robustness of our results to alternative scenarios in which either a disruption has finite duration or the crisis is foreseen. During the crisis consumers may put pressure on the Government to intervene, but too severe antispeculative measures would inefficiently discourage storage. Practical solutions to this dilemma cause welfare losses that we characterize and quantify.Storage; Dynamic models; Gas industry

Supplier selection under disaster uncertainty with joint procurement

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringJessica L. Heier StammHealth care organizations must have enough supplies and equipment on hand to adequately respond to events such as terrorist attacks, infectious disease outbreaks, and natural disasters. This is achieved through a robust supply chain system. Nationwide, states are assessing their current supply chains to identify gaps that may present issues during disaster preparedness and response. During an assessment of the Kansas health care supply chain, a number of vulnerabilities were identified, one of which being supplier consolidation. Through mergers and acquisitions, the number of suppliers within the health care field has been decreasing over the years. This can pose problems during disaster response when there is a surge in demand and multiple organizations are relying on the same suppliers to provide equipment and supplies. This thesis explores the potential for joint procurement agreements to encourage supplier diversity by splitting purchasing among multiple suppliers. In joint procurement, two or more customers combine their purchases into one large order so that they can receive quantity discounts from a supplier. This research makes three important contributions to supplier selection under disaster uncertainty. The first of these is the development of a scenario-based supplier selection model under uncertainty with joint procurement. This optimization model can be used to observe customer purchasing decisions in various scenarios while considering the probability of disaster occurrence. Second, the model is applied to a set of experiments to analyze the results when supplier diversity is increased and when joint procurement is introduced. This leads to the third and final contribution: a set of recommendations for health care organization decision makers regarding ways to increase supplier diversity and decrease the risk of disruption associated with disaster occurrence

Gas Storage and Security of Supply in the Medium Run.

This paper analyzes the role of private storage in a market for a commodity (e.g. natural gas) whose supply is subject to the threat of an irreversible disruption. We focus on the medium term in which seasonality of demand and exhaustibility can be neglected. We characterize the price and inventory dynamics (accumulation, drainage and limit stocks) in a competitive equilibrium with rational expectations . We show the robustness of our results to alternative scenarios in which either adisruption has finite duration or the crisis is foreseen. During the crisis consumers may put pressure on the Government to intervene, but too severe antispeculative measures would inefficiently discourage storage. Practical solutions to this dilemma cause welfare losses that we characterize and quantify.Gas Industry; Security of supply;

Optimizing the Domestic Chemical, Biological, Radiological, and Nuclear Response Enterprise

The Chemical Biological Radiological Nuclear (CBRN) Response Enterprise (CRE) exists to rapidly respond to a domestic CBRN attack in order to minimize the overall impact of an incident. Over the past 16 years, the CRE has grown incrementally, and it is unclear if the current locations of units optimizes the coverage of the US population within a rapid response window. In this paper we develop a multi-objective multi-service extension of the maximal covering location problem (MCLP) to analyze the current coverage provided by the CRE and recommend efficient modifications to better protect the American population. While public sector facility location problems are well studied, the significant damage created by a CBRN attack requires unique modeling considerations. Most notably, we model the impact to coverage when CRE units within a minimum stand-off distance are rendered non-functional by a CRBN attack using an adaptation of the conditional covering problem (CCP). This minimum stand-off distance is not currently a consideration in existing Department of Defense (DoD) doctrine or planning guidance, but through a comparison to the current DoD definition of coverage we demonstrate the value of incorporating this concept into future planning considerations. Finally, we account for the multi-objective nature of this problem by developing a set of non-inferior solutions that allow a decision maker to apply their judgment to balance the trade-off between coverage and cost

Developing dynamic maximal covering location problem considering capacitated facilities and solving it using hill climbing and genetic algorithm

The maximal covering location problem maximizes the total number of demands served within a maximal service distance given a fixed number of facilities or budget constraints. Most research papers have considered this maximal covering location problem in only one period of time. In a dynamic version of maximal covering location problems, finding an optimal location of P facilities in T periods is the main concern. In this paper, by considering the constraints on the minimum or maximum number of facilities in each period and imposing the capacity constraint, a dynamic maximal covering location problem is developed and two related models (A, B) are proposed. Thirty sample problems are generated randomly for testing each model. In addition, Lingo 8.0 is used to find exact solutions, and heuristic and meta-heuristic approaches, such as hill climbing and genetic algorithms, are employed to solve the proposed models. Lingo is able to determine the solution in a reasonable time only for small-size problems. In both models, hill climbing has a good ability to find the objective bound. In model A, the genetic algorithm is superior to hill climbing in terms of computational time. In model B, compared to the genetic algorithm, hill climbing achieves better results in a shorter time