Incorporating waiting time in competitive location models: Formulations and heuristics

In this paper we propose a metaheuristic to solve a new version of the Maximum Capture Problem. In the original MCP, market capture is obtained by lower traveling distances or lower traveling time, in this new version not only the traveling time but also the waiting time will affect the market share. This problem is hard to solve using standard optimization techniques. Metaheuristics are shown to offer accurate results within acceptable computing times.Market capture, queuing, ant colony optimization

Location and design of a competitive facility for profit maximisation

A single facility has to be located in competition with fixed existing facilities of similar type. Demand is supposed to be concentrated at a finite number of points, and consumers patronise the facility to which they are attracted most. Attraction is expressed by some function of the quality of the facility and its distance to demand. For existing facilities quality is fixed, while quality of the new facility may be freely chosen at known costs. The total demand captured by the new facility generates income. The question is to find that location and quality for the new facility which maximises the resulting profits. It is shown that this problem is well posed as soon as consumers are novelty oriented, i.e. attraction ties are resolved in favor of the new facility. Solution of the problem then may be reduced to a bicriterion maxcovering-minquantile problem for which solution methods are known. In the planar case with Euclidean distances and a variety of attraction functions this leads to a finite algorithm polynomial in the number of consumers, whereas, for more general instances, the search of a maximal profit solution is reduced to solving a series of small-scale nonlinear optimisation problems. Alternative tie-resolution rules are finally shown to result in ill-posed problems.Dirección General de Enseñanza Superio

When centers can fail: a close second opportunity

This paper presents the p-next center problem, which aims to locate p out of n centers so as to minimize the maximum cost of allocating customers to backup centers. In this problem it is assumed that centers can fail and customers only realize that their closest (reference) center has failed upon arrival. When this happens, they move to their backup center, i.e., to the center that is closest to the reference center. Hence, minimizing the maximum travel distance from a customer to its backup center can be seen as an alternative approach to handle humanitarian logistics, that hedges customers against severe scenario deteriorations when a center fails. For this extension of the p-center problem we have developed several different integer programming formulations with their corresponding strengthenings based on valid inequalities and variable fixing. The suitability of these formulations for solving the p-next center problem using standard software is analyzed in a series of computational experiments. These experiments were carried out using instances taken from the previous discrete location literature.Peer ReviewedPostprint (author’s final draft

On multimodality of obnoxious faclity location models

Obnoxious single facility location models are models that have the aim to find the best location for an undesired facility. Undesired is usually expressed in relation to the so-called demand points that represent locations hindered by the facility. Because obnoxious facility location models as a rule are multimodal, the standard techniques of convex analysis used for locating desirable facilities in the plane may be trapped in local optima instead of the desired global optimum. It is assumed that having more optima coincides with being harder to solve. In this thesis the multimodality of obnoxious single facility location models is investigated in order to know which models are challenging problems in facility location problems and which are suitable for site selection. Selected for this are the obnoxious facility models that appear to be most important in literature. These are the maximin model, that maximizes the minimum distance from demand point to the obnoxious facility, the maxisum model, that maximizes the sum of distance from the demand points to the facility and the minisum model, that minimizes the sum of damage of the facility to the demand points. All models are measured with the Euclidean distances and some models also with the rectilinear distance metric. Furthermore a suitable algorithm is selected for testing multimodality. Of the tested algorithms in this thesis, Multistart is most appropriate. A small numerical experiment shows that Maximin models have on average the most optima, of which the model locating an obnoxious linesegment has the most. Maximin models have few optima and are thus not very hard to solve. From the Minisum models, the models that have the most optima are models that take wind into account. In general can be said that the generic models have less optima than the weighted versions. Models that are measured with the rectilinear norm do have more solutions than the same models measured with the Euclidean norm. This can be explained for the maximin models in the numerical example because the shape of the norm coincides with a bound of the feasible area, so not all solutions are different optima. The difference found in number of optima of the Maxisum and Minisum can not be explained by this phenomenon

Locating and Protecting Facilities Subject to Random Disruptions and Attacks

Recent events such as the 2011 Tohoku earthquake and tsunami in Japan have revealed the vulnerability of networks such as supply chains to disruptive events. In particular, it has become apparent that the failure of a few elements of an infrastructure system can cause a system-wide disruption. Thus, it is important to learn more about which elements of infrastructure systems are most critical and how to protect an infrastructure system from the effects of a disruption. This dissertation seeks to enhance the understanding of how to design and protect networked infrastructure systems from disruptions by developing new mathematical models and solution techniques and using them to help decision-makers by discovering new decision-making insights. Several gaps exist in the body of knowledge concerning how to design and protect networks that are subject to disruptions. First, there is a lack of insights on how to make equitable decisions related to designing networks subject to disruptions. This is important in public-sector decision-making where it is important to generate solutions that are equitable across multiple stakeholders. Second, there is a lack of models that integrate system design and system protection decisions. These models are needed so that we can understand the benefit of integrating design and protection decisions. Finally, most of the literature makes several key assumptions: 1) protection of infrastructure elements is perfect, 2) an element is either fully protected or fully unprotected, and 3) after a disruption facilities are either completely operational or completely failed. While these may be reasonable assumptions in some contexts, there may exist contexts in which these assumptions are limiting. There are several difficulties with filling these gaps in the literature. This dissertation describes the discovery of mathematical formulations needed to fill these gaps as well as the identification of appropriate solution strategies

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

Spatial organization of public services: models and applications

Location decisions are crucial in the spatial organization in both public and private sectors as they can have a long term impact on operational performances and on service levels. Social cost minimization, universality of services and equity, expressed in terms of users' accessibility, are the main objectives in public services contexts. Nevertheless, the enduring trend of public expenditures revision poses, also in the public sectors, the need to pursue objectives of economic efficiency. In the literature, two families of optimization problems are typically used to address these problems, namely Facility Location Problems (FLPs) and Districting Problems (DPs). The aim of this thesis is to show how FLPs and DPs can be used to underpin spatial organization processes of public services, providing analytical models able to assist the decision making. To this end, novel mathematical models are developed with application to the healthcare and postal service sectors. In particular, a hierarchical facility location model is formulated to reorganize an existing regional Blood Management System (BMS) while an integrated location-districting model is proposed for the organization of postal collection operations in urban areas. A constructive heuristic procedure is also devised to solve the latter problem. Extensive computational experiments are realized to validate the proposed models and to show their capability to provide insightful managerial implications. Finally, the thesis aims at filling another existing gap in the literature due to the absence of stochastic models for DPs. Hence, a two-stage stochastic program for districting is introduced and tested on real georgaphic data. Several extensions of the proposed modeling framework are also discussed