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

Zaman pencereli ve değişken başlama zamanlı bir araç rotalama problemi için sütun türetme temelli matsezgiseller

In this study, a vehicle routing problem with time windows is investigated, where the costs depend on the total duration of vehicle routes and the starting time from the depot for each vehicle is determined by a decision maker. In order to solve the problem, two column generation based mat-heuristics are developed, where the first one makes use of the iterated local search and the second one uses the variable neighbourhood search. In order to assess the accuracy of the mat-heuristics, they are first compared with an exact algorithm on small instances taken from the literature. Since their performance are quite satisfactory, they are further tested on 87 large instances by running each algorithm 3 times for each instance. The computational results prove that the mat-heuristic using the variable neighbourhood search outperforms the other one. Hence, this enables to obtain a good feasible solution in a very short time when it is not possible to solve large instances with an exact solution method in a reasonable CPU time.Bu çalışmada, araçların kullanıldıkları süreye bağlı maliyetlerin oluştuğu ve araçların depodan başlama zamanının bir karar verici tarafından belirlendiği zaman pencereli bir araç rotalama problemi ele alınmaktadır. Problemi çözmek için biri yinelemeli yerel arama meta-sezgiselinden, diğeri değişken komşuluk arama meta-sezgiselinden yararlanan iki sütun türetme temelli mat-sezgisel geliştirilmiştir. Geliştirilen mat-sezgiseller ilk önce literatürden alınarak türetilen küçük bir veri kümesi üzerinde problemin eniyi sonucunu bulan kesin bir yöntem ile karşılaştırılarak kaliteli sonuçlar ürettiklerini kanıtlamışlardır. Yöntemlerin ürettikleri sonuçların doğruluk derecesinden emin olunduktan sonra, daha büyük 87 örnek üzerinde her mat-sezgisel her örnekte 3 kere çalıştırılarak test edilmiştir. Bilgisayımsal sonuçlar değişken komşuluk arama meta-sezgiseli kullanan mat-sezgiselin, daha kaliteli ve verimli sonuçlar vererek daha başarılı bir algoritma olduğunu göstermiştir. Bu sayede kesin bir yöntemle makul bir ana işlemci zamanında çözülemeyen büyük ölçülü problemler için çok kısa bir zaman içerisinde iyi bir olurlu çözüm elde etmek mümkün hale gelmiştir.TÜBİTAK BİDEBWOS:000486923100003Scopus - Affiliation ID: 60105072TR - DizinScience Citation Index ExpandedQ4ArticleUluslararası işbirliği ile yapılmayan - HAYIROcak2019YÖK - 2018-1

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

Die dichte basierten methoden für die cluster analyse

KÜMELEME ANALİZİNDE YOĞUNLUK TEMELLİ METOTLAR Akademisyenler ve pazar araştırmacıları, homojen obje gruplarının tanımıyla en iyi şekilde çözülebilecek durumlarla sık sık karşılaşmaktadılar; bu objelerin kişiler, kurumlar, ürünler veya davranışlar olması ise fark yaratmamaktadır. Segmentlere ayırma ve hedef pazarlamada olduğu gibi popülasyon içindeki grupların teşhisine dayanan strateji opsiyonları nesnel bir metodoloji olmadan mümkün olamazdı. Aynı ihtiyaca başka alanlarda da rastlanmaktadır. Bütün örneklerde gözlemler arasında “doğal bir yapı” aranmaktadır. Bu amaç doğrultusunda genellikle “Kümeleme Analizi” tekniği kullanılmaktadır. Bu çalışmada bir veri tabanını küme bileşenlerine ayırabilmek için yoğunluk temelli genel bir metot tanıtılmıştır. Bu metot her türlü veri tipine sahip objelerde şu iki koşulla uygulanabilmektedir: (1) objeler için simetrik ve dönüşlü, ikili bir yüklem ve (2) kullanıcıya, bir obje kümesinin minimum ağırlığı olup olmadığını tespit etmesine izin veren bir yüklem bulunmalıdır. Dozenten und Marktforscher stoßen meistens auf die Situationen, die am besten von der Definition der Gruppen von homogenen Objekten gelöst werden können, egal ob diese Objekte Individuen, Unternehmen, Produkte oder sogar Verhalten sind. Strategie-Optionen basiert auf die Identifizierung der Gruppen innerhalb der Population wie die Segmentierung und Zielmarketing würden ohne eine objektive Methodologie nicht möglich sein. Dem gleichen Bedürfnis wird auch in vielen anderen Bereichen getroffen. In allen Instanzen sucht man eine „natürliche Struktur “ unter den Beobachtungen nach. Zu diesem Zweck wird meistens die Technik „Cluster Analyse“ verwendet. In dieser Arbeit wird eine allgemeine Dichte-basierte Methode vorgestellt, die auf Objekten von beliebigen Datentyp anwendbar ist, vorausgesetzt, dass (1) es ein binäres (Nachbarschaft) Prädikat für Objekte, das symmetrisch und reflexiv ist, und (2) ein Prädikat gibt, das dem Benutzer erlaubt, zu bestimmen, ob eine Menge von Objekten „minimales Gewicht“ hat oder nicht, um eine Datenbank in eine Menge von Cluster-Komponenten zu zerlegen

Optimization of the service start time for an elementary shortest path problem with time windows

We investigate an elementary shortest path problem with resource constraints where a single capacitated vehicle, initially located at a depot, must serve a set of customers while respecting their individual time windows. When the vehicle visits a customer, it delivers the customer's demand and collects a revenue in return for the delivery. The vehicle can start its trip at any desired time. The transportation cost is a function of both the total distance traveled and the duration of the assigned trip. The objective is to determine the service start time from the depot, the subset of customers to be served, and the trip to be performed so as to minimize the total loss, which is calculated as the di erence between the transportation cost and the revenue collected from the customers. We develop two exact dynamic programming algorithms which can deal with an in nite number of Pareto-optimal states arising from the fact that the starting time and the duration of the trip act like continuous decision variables. We report computational results obtained with these algorithms and with a faster heuristic for the elementary shortest path problem. We also examine the performance of these algorithms when they are used to solve the pricing subproblem arising in the framework of a column generation algorithm for a related vehicle routing problem with time windows

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

A comparative study of branch-and-price algorithms for a vehicle routing problem with time windows and waiting time costs

Branch-and-price is a leading methodology for solving routing problems. Several studies have investigated labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Solving this problem efficiently is crucial, since it is a performance bottleneck for the branch-and-price procedure. Such algorithms include methods like decremental state space relaxation, ng-route relaxation, and hybrids of these two. These focus on how to treat efficiently the elementarity constraints, since they tend to make label domination difficult, which translates to more computational resources used. In this study, we investigate the performance of these methods in a branch-and-price framework. The problem under consideration is a variant of the vehicle routing problem with time windows in which waiting times have a linear cost. We first parametrize several algorithmic components. Then, we search for good parameter configurations for each algorithm with irace, a tool for automated parameter tuning that generates and runs a very high number of configurations on a set of tuning instances and uses statistical tests to determine the best performing configuration. Finally, we run all final configurations on the Solomon benchmark instances and analyze the results with statistical tests. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others

A comparative study of branch-and-price algorithms for vehicle routing with time windows and waiting time costs

Branch-and-price is a leading methodology for solving routing problems. Several studies have investigated labeling algorithms to solve the related pricing problem, which is usually a variant of the elementary shortest path problem with resource constraints. Solving this problem efficiently is crucial, since it is a performance bottleneck for the branch-and-price procedure. Such algorithms include methods like decremental state space relaxation, ng-route relaxation, and hybrids of these two. These focus on how to treat efficiently the elementarity constraints, since they tend to make label domination difficult, which translates to more computational resources used. In this study, we investigate the performance of these methods in a branch-and-price framework. The problem under consideration is a variant of the vehicle routing problem with time windows in which waiting times have a linear cost. We first parametrize several algorithmic components. Then, we search for good parameter configurations for each algorithm with irace, a tool for automated parameter tuning that generates and runs a very high number of configurations on a set of tuning instances and uses statistical tests to determine the best performing configuration. Finally, we run all final configurations on the Solomon benchmark instances and analyze the results with statistical tests. Our results show that a class of hybrid algorithms with certain features based on ng-route relaxation outperforms all the others

Column generation based algorithms for a VRP with time windows & variable departure times

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