Determining the most vulnerable components in a transportatıon network

Transportation networks belong to the class of critical infrastructure networks since a small deterioration in the service provision has the potential to cause considerable negative consequences on everyday activities. Among the reasons for the deterioration we can mention the shutdown of a subway station, the closure of one or more lanes on a bridge, the operation of an airport at a much reduced capacity. In order to measure the vulnerability of transportation network, it is necessary to determine the maximum possible disruption by assuming that there is an intelligent attacker wishing to give damage to the components of the network including the stations/stops and linkages. Identifying the worst disruptions can be realized by using interdiction models that are formulated by a bilevel mathematical programming model involving two decision makers: leader and follower. In this paper, we develop such a model referred to as attacker-operator model, where the leader is a virtual attacker who wants to cause the maximum possible disruption in the transportation network by minimizing the amount of flow among the nodes of the network, while the follower is the system operator who tries to reorganize the flow in the most effective way by maximizing the flow after the disruption. The benefit of such a model to the system operator is to determine the most vulnerable stations and linkages in the transportation network on one hand, and to take precautions in preventing the negative effects of the disruption on the other hand.TUBİTAK Sponsorlu YayınWOS:000458617100002Emerging Sources Citation IndexArticleOcak2018YÖK - 2017-1

Bilevel models on the competitive facility location problem

Facility location and allocation problems have been a major area of research for decades, which has led to a vast and still growing literature. Although there are many variants of these problems, there exist two common features: finding the best locations for one or more facilities and allocating demand points to these facilities. A considerable number of studies assume a monopolistic viewpoint and formulate a mathematical model to optimize an objective function of a single decision maker. In contrast, competitive facility location (CFL) problem is based on the premise that there exist competition in the market among different firms. When one of the competing firms acts as the leader and the other firm, called the follower, reacts to the decision of the leader, a sequential-entry CFL problem is obtained, which gives rise to a Stackelberg type of game between two players. A successful and widely applied framework to formulate this type of CFL problems is bilevel programming (BP). In this chapter, the literature on BP models for CFL problems is reviewed, existing works are categorized with respect to defined criteria, and information is provided for each work.WOS:000418225000002Scopus - Affiliation ID: 60105072Book Citation Index- Science - Book Citation Index- Social Sciences and HumanitiesArticle; Book ChapterOcak2017YÖK - 2016-1

Gradual covering location problem with multi-type facilities considering customer preferences

In this paper, we address a discrete facility location problem where a retailer aims at locating new facilities with possibly different characteristics. Customers visit the facilities based on their preferences which are represented as probabilities. These probabilities are determined in a novel way by using a fuzzy clustering algorithm. It is assumed that the sum of the probabilities with which customers at a given demand zone patronize different types of facilities is equal to one. However, among the same type of facilities they choose the closest facility, and the strength at which this facility covers the customer is based on two distances referred to as full coverage distance and gradual (partial) coverage distance. If the distance between the customer location and the closest facility is smaller (larger) than the full (partial) coverage distance, this customer is fully (not) covered, whereas for all distance values between full and partial coverage, the customer is partially covered. Both distance values depend on both the customer attributes and the type of the facility. Furthermore, facilities can only be opened if their revenue exceeds a certain threshold value. A final restriction is incorporated into the model by defining a minimum separation distance between the same facility types. This restriction is also extended to the case where a minimum threshold distance exists among facilities of different types. The objective of the retailer is to find the optimal locations and types of the new facilities in order to maximize its profit. Two versions of the problem are formulated using integer linear programming, which differ according to whether the minimum separation distance applies to the same facility type or different facility types. The resulting integer linear programming models are solved by three approaches: commercial solver CPLEX, heuristics based on Lagrangean relaxation, and local search implemented with 1-Add and 1-Swap moves. Apart from experimentally assessing the accuracy and the efficiency of the solution methods on a set of randomly generated test instances, we also carry out sensitivity analysis using a real-world problem instance.WOS:000566574300006Scopus - Affiliation ID: 60105072Science Citation Index Expanded - Social Sciences Citation IndexQ1ArticleUluslararası işbirliği ile yapılmayan - HAYIREylül2020YÖK - 2020-2

Two-stage stochastic integer programming models for strategic disaster preparedness

editorial reviewedWe are interested in two distinct disaster management models which are modeled by two-stage stochastic integer programming. While the first model solution provides optimal post-disaster response related decisions and pre-disaster retrofitting decisions to minimize the total cost, in addition to the first model, the second model considers the recovery actions and the time of earthquake occurrence. In the models, retrofitting decisions are given for both buildings and bridges under a limited budget. Effective and efficient solution methods are proposed in the study

A Study on Different Facility Location Problems

The main focus of this study is on competitive facility location problems which constitute a special family of facility location problems. In such a problem, a firm or franchise is concerned with installing new facilities to serve customers in a market where existing facilities with known locations and attractiveness levels compete for increasing their market share and profit. We can classify these problems into two groups: those with non-reactive competition and those with reactive competition. In this study, three different types of competitive facility location models are proposed in order to determine the locations and attractiveness levels of the new facilities to maximize the profit. The first one belongs to the former class, where the last two models fall into the latter one and therefore bring us to the area of the bilevel programming. Finally, a different facility location problem which takes the customer preferences into account is considered, where the facilities are not necessarily identical and customers visit different types of facilities according to some given probability distribution and the maximum distance which they are willing to travel

Tabu Search Heuristics for a Bilevel Competitive Facility Location Problem

peer reviewedIn this study, the problem of a firm is considered where the firm tries to open new facilities in a market where there are already existing facilities belonging to a competitor. The new entrant firm wishes to find the optimal location and attractiveness levels of its facilities to maximize its profit. On the other hand, the competitor can react to the new entrant by changing the attractiveness levels of its existing facilities, closing them and/or opening new facilities. The gravity-based rule is employed in order to model the customer behavior. According to this rule, the probability that a customer patronizes a facility is proportional to the attractiveness level of the facility and inversely proportional to the distance between the customer and the facility. To this end, a bilevel mixed-integer nonlinear programming problem in discrete space is formulated. The new entrant firm is the leader of the game and the competitor is the follower. In order to find feasible solutions to the model, two tabu search heuristic methods are proposed. Two exact methods are utilized as subroutines of the proposed methods: a gradient ascent algorithm and a branch-and-bound algorithm that uses nonlinear programming relaxation