Athanasia Karakitsiou

Locating facilities in a competitive environment

The research work dealing with the bi-level formulation of location problems is limited only to the competition among the locators, that is, it is supposed that either both the locator and the allocator are the same or the customer knows the optimality criterion of the locator and agrees passively with it. Customers’ preferences as well as externalities (such as road congestion, facility congestion, emissions etc) caused by the location decisions are either ignored or controlled by incorporating constraints in order to ensure the achievement of a predetermined target. However, this approach treats customers as irresolute beings. Thus, if, for example, the customers travel to the facilities to obtain the offered service, then there is no compulsion or intensive for them to attend the designated facility. This means that, once the facilities are open, what the locator wishes the customers to do may not coincide with their own wish and behavior. We suppose that the customers are involved in a Nash game in order to ensure what they conceive as the best level of services for themselves. In order to take into consideration the effects of such competition in the facilities location decisions we propose a bi-level programming approach to the problem.

Convex optimization problems in supply chain planning and their solution by a column generation method based on the Frank Wolfe method

Many problems in supply chain optimization concern the minimization of a differentiable convex objective function subject to a set of linear constraints. The aim of this work is to present a number of such problems and to propose an efficient method for their solution. The proposed method is based on improvements of the well known Frank–Wolfe algorithm. The computational results of the proposed algorithm demonstrate its effectiveness and efficiency.

Optimization, Control, and Applications in the Information Age: In Honor of Panos M. Pardalos's 60th Birthday

Recent developments in theory, algorithms, and applications in optimization and control are discussed in this proceedings, based on selected talks from the Optimization Control and Applications in the Information Age conference,organized in honor of Panos Pardaloss 60th birthday. This volume contains numerous applications to optimal decision making in energy production and fuel management, data mining, logistics, supply chain management, market network analysis, risk analysis, and community network analysis. In addition, a short biography is included describing Dr. Pardaloss path from a shepherd village on the high mountains of Thessaly to academic success.Due to the wide range of topics such as global optimization, combinatorial optimization, game theory, stochastics and programming contained in this publication, scientists, researchers, and students in optimization, operations research, analytics, mathematics and computer science will be interested in this volume.

Discrete Competitive Facility Location: Modeling and Optimization Approaches

Competitive facility location problems are concerned with the following situation: a firm wants to locate a predefined number of facilities to serve customers located in some region where there already exist (or will be) other firms offering the same service. Both new and existing firms compete for optimizing their market share of profit. A discrete version of such problems arises when it is assumed that there are a (rather small) finite number of candidate locations and the markets consist of point demands. We review modeling and optimization approaches for this type of problems and we emphasize and develop the bi-level programming methodology.

Massive datasets and machine learning for computational biomedicine: trends and challenges

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine.

Nash type games in competitive facilities location

In a non-cooperative game, a Nash equilibrium corresponds to a set of strategies, which ensure that none of the players are better off by unilaterally changing his strategy. This work focuses on th ...

Competitive Facility Location with Competition of Customers

The research work dealing with the competitive location problems is limited only to the competition among the locators, that is, it is supposed that either both the locator and the allocator are the same or the customer knows the optimality criterion of the locator and agrees passively with it. Customers preferences as well as externalities (such as road congestion, facility congestion, emissions, etc.) caused by the location decisions are either ignored or “controlled” by incorporating constraints in order to “ensure” the achievement of a predetermined target. However, this approach treats customers as irresolute beings. Thus, if, for example, the customers travel to the facilities to obtain the offered service, then there is no compulsion or incentive for them to attend the designated facility. This means that, once the facilities are open, what the locator wishes the customers to do may not coincide with their own wish and behavior.

Simultaneous Competitive Facility Location Models

The research effort of this body of literature aims at developing insights concerning the equilibrium pattern of location decisions and their stability. Another subject that is addressed by several authors is the existence or (not) of a set of locations and pricing or production quantities that will ensure a Nash equilibrium, that is, a position where neither firms have incentives to move.

Sequential Competitive Facility Location Problems

The formalization of this class of problem and the fundamental complexity results were established by Hakimi [28]. Following the game introduced by von Stackelberg [76], Hakimi [28] presented the two basic problems in sequential location analysis, the centroid and medianoid problems. These two problems are faced by the leader and the follower, respectively. The leader attempts to locate p( ≥ 1) facilities knowing that a follower will in turn locate his r( ≥ 1) facilities based on the leader’s chosen locations; this is the (r | p)-centroid problem. The follower knows the set X p that indicates where the leader’s facilities are located, and solves an (r | X p )-medianoid problem. Customers choose among the facilities according to a function of the distance between themselves and the facilities, preferring always the closest. This is the so-called binary customer choice. The formulation of the problems is based on the assumption that co-location is not allowed and if, by any chance the distance from a customer to the closest facility of the two competitors is the same, the customer always prefers the leader’s facility. The demand of the customer is also considered to be inelastic with respect to the distance traveled.

Static Competitive Facility Location Problems

In this section we review some of the most important contribution to the field of static competitive facility location problem. The main assumptions of the models presented in this section can be summarized as follows: the existing competition is known and fixed, the product sold is homogeneous, the customers’ decision in patronizing the facility is based entirely on the distance traveled. the unit costs are the same in all stores regardless of ownership