p-facility Huff location problem on networks

The p-facility Huff location problem aims at locating facilities on a competitive environment so as to maximize the market share. While it has been deeply studied in the field of continuous location, in this paper we study the p-facility Huff location problem on networks formulated as a Mixed Integer Nonlinear Programming problem that can be solved by a branch-and-bound algorithm. We propose two approaches for the initialization and division of subproblems, the first one based on the straightforward idea of enumerating every possible combination of p edges of the network as possible locations, and the second one defining sophisticated data structures that exploit the structure of the combinatorial and continuous part of the problem. Bounding rules are designed using DC (difference of convex) and Interval Analysis tools. In our computational study we compare the two approaches on a battery of 21 networks and show that both of them can handle problems for p ≤ 4 in reasonable computing time.Ministerio de Economía y CompetitividadJunta de AndalucíaHungarian National Research, Development and Innovation OfficeInformation and Communication Technologies COS

A new chance-constrained maximum capture location problem

The paper presents a new model based on the basic Maximum Capture model, MAXCAP. The New Chance–Constrained Maximum Capture modelintroduces a stochastic threshold constraint, which recognises the fact that a facility can be open only if a minimum level of demand is captured. A metaheuristic based on MAX–MIN ANT system and TABU search procedure is presented to solve the model. This is the first time that the MAX–MIN ANT system is adapted to solve a location problem. Computational experience and an application to 55–node network are also presented.Stochastic location, capture models

Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporates theories of Consumer Choice Behavior in its models. Following this direction, this paper studies the importance of consumer behavior with respect to distance or transportation costs in the optimality of locations obtained by traditional Competitive Location models. To do this, it considers different ways of defining a key parameter in the basic Maximum Capture model (MAXCAP). This parameter will reflect various ways of taking into account distance based on several Consumer Choice Behavior theories. The optimal locations and the deviation in demand captured when the optimal locations of the other models are used instead of the true ones, are computed for each model. A metaheuristic based on GRASP and Tabu search procedure is presented to solve all the models. Computational experience and an application to 55-node network are also presented.Distance, competitive location models, consumer choice behavior, GRASP, tabu

Determining and evaluating new store locations using remote sensing and machine learning

Decision making for store locations is crucial for retail companies as the profit depends on the location. The key point for correct store location is profit approximation, which is highly dependent on population of the corresponding region, and hence, the volume of the residential area. Thus, estimating building volumes provides insight about the revenue if a new store is about to be opened there. Remote sensing through stereo/tri-stereo satellite images provides wide area coverage as well as adequate resolution for three dimensional reconstruction for volume estimation. We reconstruct 3D map of corresponding region with the help of semiglobal matching and mask R-CNN algorithms for this purpose. Using the existing store data, we construct models for estimating the revenue based on surrounding building volumes. In order to choose the right location, the suitable utility model, which calculates store revenues, should be rigorously determined. Moreover, model parameters should be assessed as correctly as possible. Instead of using randomly generated parameters, we employ remote sensing, computer vision, and machine learning techniques, which provide a novel way for evaluating new store locations.WOS:000679318000002Scopus - Affiliation ID: 60105072Science Citation Index ExpandedScience Citation Index ExpandedQ4ArticleArticleUluslararası işbirliği ile yapılmayan - HAYIRAğustos2021YÖK - 2020-2

Systematic approaches for retail service location decisions

Text includes handwritten formulas. No pages 18, 92This thesis investigates systems applications to community facility planning by focusing on the use of models in locating retail facilities. This approach was taken because a number of major concepts employed in retail location are directly transferable to most types of urban services where consumers may choose to utilize a number of different locations. A general decision making process for locating retail services is described. Review of the types of information needed by a retail location planner finds that the central issue he faces is estimating the sales volume of a proposed site. The effect of other competing locations, consumer preferences and accessibility make this task difficult without some type of systematic approach. This could be called the classical "problem" of retail location. An extensive search was made of the work of others related to this problem. A number of approaches were found which attempted to represent the interrelated elements of consumers, access, and retailers which constitute a retail system. No dominant theory has been developed in the area; instead, a number of individual lines of inquiry were found with similarities between. Several selected location models are then reviewed in application to specific problems. The major criticism provided focusses on the degree of difficulty model authors have in representing consumer-retailer behavior and the type of information required to support the modelling. It was found that no one type of model can be regarded as superior since each may have been developed for different planning applications which vary in type of retail service and geographic area represented. There are other steps in retail location decision making where further applications of systems approaches may be valuable. These include population and income forecasting for a small area and economic evaluation of location alternatives once gross sales have been estimated. Further development of these areas in conjunction with the retail models described is suggested. Finally, a number of concepts found in various approaches to retail location may have direct benefit in the successful application of planning standards commonly used by architects and urban designers. Insight gained through certain theoretical approaches to retail location imply that increased care should be taken in the derivation and application of meaningful planning standards