Hierarchical location-allocation models for congested systems

In this paper we address the issue of locating hierarchical facilities in the presence of congestion. Two hierarchical models are presented, where lower level servers attend requests first, and then, some of the served customers are referred to higher level servers. In the first model, the objective is to find the minimum number of servers and their locations that will cover a given region with a distance or time standard. The second model is cast as a Maximal Covering Location formulation. A heuristic procedure is then presented together with computational experience. Finally, some extensions of these models that address other types of spatial configurations are offered.Hierarchical location, congestion, queueing

A Proposal to Improve the Health Care Systems for the Urban Poor in the Squatter Settlements of the Developing Countries

Rapid urbanization and large scale population movements from rural to urban areas have resulted in unprecedented health crises in the developing countries. In addition to communicable diseases, respiratory infections and malnutrition, psycho-social stresses due to marginalization and exclusion from social activities and employment prospects are also prevalent. Considering the rate of urban growth rate and the rapid increase in the percentage of the poor living in urban areas, the debilitating effects of health crises and urban poverty are going to exacerbate if no precautions are taken. In this respect, it is a critical point in time to come up with effective health care strategies for the urban poor. This document provides an insight into the reasons behind the current health problems of the urban poor and the determinants of health in developing countries, and proposes use of operations research to come up with handling strategies for the major subdivisions of the health problem in the developing world

Comparison of Emergency Medical Services Delivery Performance using Maximal Covering Location and Gradual Cover Location Problems

Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered

A queuing location-allocation model for a capacitated health care system

International audienceThe aim of the present paper is to propose a location-allocation model for a capacitated health care system. This paper develops a discrete modeling framework to determine the optimal number of facilities among candidates and optimal allocations of the existing customers to operating health centers in a coverage distance. In doing so, the total sum of customer and operating facility costs is minimized. Our goal is to create a model that is more practical in the real world. Therefore, setup costs of hospitals are based on the costs of customers, xed costs of establishing health centers, and costs based on the available resources in each level of hospitals. In this paper, the idea of hierarchical structure has been used. There are two levels of service in hospitals, including low and high levels, and sections at diierent levels that provide diierent types of services. The patients refer to diierent sections of the hospital according to their requirements. To solve the model, two meta-heuristic algorithms, including genetic and simulated annealing algorithms and their combination, are proposed. To evaluate the performance of the three algorithms, some numerical examples are produced and analyzed using the statistical test in order to determine which algorithm works better

An exact Method for Stochastic Maximal Covering Problem of Preventive Healthcare Facilities

Abstract Effective preventive healthcare services have a significant role in reducing fatality and medical expenses in all human societies and the level of accessibility of customers to these services can be considered as a measure of their efficiency and effectiveness. The main purpose of this paper is to develop a service network design model of preventive healthcare facilities with the principal objective of maximizing participation in the offered services. While considering utility constraints and incorporating demand elasticity of customers due to travel distance and congestion delays, optimal number, locations and capacities of facilities as well as customer assignment o facilities are determined. First, the primary nonlinear integer program is transformed, and then the linearized model is solved by developing an exact algorithm. Computational results show that large-sized instances can be solved in a reasonable amount of time. An illustrative case study of network of hospitals in Shiraz, Iran, is used to demonstrate the model and the managerial insights are discussed

A Decomposition Heuristic for the Maximal Covering Location Problem

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature

A Decomposition Heuristic for the Maximal Covering Location Problem

This paper proposes a cluster partitioning technique to calculate improved upper bounds to the optimal solution of maximal covering location problems. Given a covering distance, a graph is built considering as vertices the potential facility locations, and with an edge connecting each pair of facilities that attend a same client. Coupling constraints, corresponding to some edges of this graph, are identified and relaxed in the Lagrangean way, resulting in disconnected subgraphs representing smaller subproblems that are computationally easier to solve by exact methods. The proposed technique is compared to the classical approach, using real data and instances from the available literature

A bi-level model and solution methods for partial interdiction problem on capacitated hierarchical facilities

Due to the importance of gaining high levels of customer satisfaction in today's competitive world, making appropriate decisions in the face of malicious attacks is valued highly by many organizations. In this paper, to predict and handle the destructive effects of an intentional attack on capacitated nested hierarchical facilities, a bi-level partial interdiction problem is proposed. In this problem, there is an interdictor who can attack facilities partially in different levels. Subsequently, the system defender could respond to the customers’ demand in two different ways, namely through the remaining system facilities and the outsourcing option. The goal of the defender is to minimize the satisfaction cost of all customers’ demand under the interdictor's attacking scenario. This problem can be modeled as a bi-level programming model in which an interdictor and the system defender play the role of the leader and the follower, respectively. Due to the inherent complexity of the bi-level programming models, we develop a heuristic approach, namely “FDS”, to obtain near optimal solutions within a reasonable running time. In each iteration of the FDS, an interdiction scenario is produced heuristically and, thereupon CPLEX solver is called to solve the lower level of the model. To evaluate the effectiveness of the proposed model, a comparison between the cost of customers’ demand satisfaction in both absence and presence of the bi-level model is drawn. Computational results show that for those instances in which the optimal solutions are available, the proposed model can, on average, achieve a saving of 7.94%

Hybrid Set Covering and Dynamic Modular Covering Location Problem: Application to an Emergency Humanitarian Logistics Problem

This paper presents an extension of the covering location problem as a hybrid covering model that utilizes the set covering and maximal covering location problems. The developed model is a multi-period model that considers strategic and tactical planning decisions. Hybrid covering location problem (HCLP) determines the location of the capacitated facilities by using dynamic set covering location problem as strategic decisions and assigns the constructive units of facilities and allocates the demand points by using dynamic modular capacitated maximal covering location problem as tactical decisions. One of the applications of the proposed model is locating first aid centers in humanitarian logistic services that have been addressed by studying a threat case study in Japan. In addition to validating the developed model, it has been compared to other possible combined problems, and several randomly generated examples have been solved. The results of the case study and model validation tests approve that the main hybrid developed model (HCLP) is capable of providing better coverage percentage compared to conventional covering models and other hybrid variants