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.

Location models in the public sector

The past four decades have witnessed an explosive growth in the field of networkbased facilitylocation modeling. This is not at all surprising since location policy is one of the mostprofitable areas of applied systems analysis in regional science and ample theoretical andapplied challenges are offered. Location-allocation models seek the location of facilitiesand/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or severalobjectives 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, thatare related to the public sector, such as emergency services (ambulances, fire stations, andpolice 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 onthe P-Median problem and some of the issues faced by planners when implementing thisformulation in real world locational decisions. Finally, the last section will examine newtrends in public sector facility location modeling.

Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporatestheories of Consumer Choice Behavior in its models. Following thisdirection, this paper studies the importance of consumer behavior withrespect to distance or transportation costs in the optimality oflocations obtained by traditional Competitive Location models. To dothis, it considers different ways of defining a key parameter in thebasic Maximum Capture model (MAXCAP). This parameter will reflectvarious ways of taking into account distance based on several ConsumerChoice Behavior theories. The optimal locations and the deviation indemand captured when the optimal locations of the other models are usedinstead of the true ones, are computed for each model. A metaheuristicbased on GRASP and Tabu search procedure is presented to solve all themodels. Computational experience and an application to 55-node networkare also presented.

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 CaptureProblem. In the original MCP, market capture is obtained by lower traveling distances or lowertraveling time, in this new version not only the traveling time but also the waiting time willaffect the market share. This problem is hard to solve using standard optimization techniques.Metaheuristics are shown to offer accurate results within acceptable computing times.

Location models for ceding market share and shrinking services

New location models are presented here for exploring the reduction of facilities in aregion. The first of these models considers firms ceding market share to competitorsunder situations of financial exigency. The goal of this model is to cede the leastmarket share, i.e., retain as much of the customer base as possible while sheddingcostly outlets. The second model considers a firm essentially without competition thatmust shrink it services for economic reasons. This firm is assumed to close outlets sothat the degradation of service is limited. An example is offered within a competitiveenvironment to demonstrate the usefulness of this modeling approach.

Location models for airline hubs behaving as M/D/c queues

Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.

Methodological issues in applying Location Models to Rural areas

Location Models are usedfor planning the location of multiple service centers in order to serve a geographicallydistributed population. A cornerstone of such models is the measure of distancebetween the service center and a set of demand points, viz, the location of thepopulation (customers, pupils, patients and so on). Theoretical as well asempirical evidence support the current practice of using the Euclidian distancein metropolitan areas. In this paper, we argue and provide empirical evidencethat such a measure is misleading once the Location Models are applied to ruralareas with heterogeneous transport networks. This paper stems from the problemof finding an optimal allocation of a pre-specified number of hospitals in alarge Swedish region with a low population density. We conclude that the Euclidianand the network distances based on a homogenous network (equal travel costs inthe whole network) give approximately the same optimums. However networkdistances calculated from a heterogeneous network (different travel costs indifferent parts of the network) give widely different optimums when the numberof hospitals increases.  In terms ofaccessibility we find that the recent closure of hospitals and the in-optimallocation of the remaining ones has increased the average travel distance by 75%for the population. Finally, aggregation the population misplaces the hospitalsby on average 10 km.

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.

Location model for CCA-treated - wood waste remediation units

Dissertação apresentada como requisito parcial para obtenção do grau de Mestre em Ciência e Sistemas de Informação Geográfica

What underlies localization and urbanization economies? Evidence from the location of new firms

The objective of this paper is to analyze why firms in some industries locate in specialized economic environments (localization economies) while those in other industries prefer large city locations (urbanization economies). To this end, we examine the location decisions of new manufacturing firms in Spain at the city level and for narrowly defined industries (three-digit level). First, we estimate firm location models to obtain estimates that reflect the importance of localization and urbanization economies in each industry. In a second step, we regress these estimates on industry characteristics that are related to the potential importance of three agglomeration theories, namely, labor market pooling, input sharing and knowledge spillovers. Localization effects are low and urbanization effects are high in knowledge-intensive industries, suggesting that firms (partly) locate in large cities to reap the benefits of inter-industry knowledge spillovers. We also find that localization effects are high in industries that employ workers whose skills are more industry-specific, suggesting that industries (partly) locate in specialized economic environments to share a common pool of specialized workers.

New Trends in Public Facility Location Modeling

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.

Locating emergency services with priority rules: The priority queuing covering location problem

One of the assumptions of the Capacitated Facility Location Problem (CFLP) is thatdemand is known and fixed. Most often, this is not the case when managers take somestrategic decisions such as locating facilities and assigning demand points to thosefacilities. In this paper we consider demand as stochastic and we model each of thefacilities as an independent queue. Stochastic models of manufacturing systems anddeterministic location models are put together in order to obtain a formula for thebacklogging probability at a potential facility location.Several solution techniques have been proposed to solve the CFLP. One of the mostrecently proposed heuristics, a Reactive Greedy Adaptive Search Procedure, isimplemented in order to solve the model formulated. We present some computationalexperiments in order to evaluate the heuristics performance and to illustrate the use ofthis new formulation for the CFLP. The paper finishes with a simple simulationexercise.

Supermarket key attributes and location decisions: A comparative study between British and Spanish consumers

The Maximum Capture problem (MAXCAP) is a decision model that addresses the issue of location in a competitive environment. This paper presents a new approach to determine which store s attributes (other than distance) should be included in the newMarket Capture Models and how they ought to be reflected using the Multiplicative Competitive Interaction model. The methodology involves the design and development of a survey; and the application of factor analysis and ordinary least squares. Themethodology has been applied to the supermarket sector in two different scenarios: Milton Keynes (Great Britain) and Barcelona (Spain).