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Publication Metadata only A matheuristic for leader-follower games involving facility location-protection-interdiction decisions(Springer, 2013) Aras, Necati; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308The topic of this chapter is the application of a matheuristic to the leaderfollower type of games-also called static Stackelberg games-that occur in the context of discrete location theory. The players of the game are a system planner (the leader) and an attacker (the follower). The decisions of the former are related to locating/relocating facilities as well as protecting some of those to provide service. The attacker, on the other hand, is interested in destroying (interdicting) facilities to cause the maximal possible disruption in service provision or accessibility. The motivation in the presented models is to identify the facilities that are most likely to be targeted by the attacker, and to devise a protection plan to minimize the resulting disruption on coverage as well as median type supply/demand or service networks. Stackelberg games can be formulated as a bilevel programming problem where the upper and the lower level problems with conflicting objectives belong to the leader and the follower, respectively. In this chapter, we first discuss the state of the art of the existing literature on both facility and network interdiction problems. Secondly, we present two fixed-charge facility location-protection-interdiction models applicable to coverage and median-type service network design problems. Out of these two, we focus on the latter model which also involves initial capacity planning and post-attack capacity expansion decisions on behalf of the leader. For this bilevel model, we develop a matheuristic which searches the solution space of the upper level problem according to tabu search principles, and resorts to a CPLEXbased exact solution technique to tackle the lower level problem. In addition, we also demonstrate the computational efficiency of using a hash function, which helps to uniquely identify and record all the solutions visited, thereby avoids cycling altogether throughout the tabu search iterationsPublication Metadata only A multi-start granular skewed variable neighborhood tabu search for the roaming salesman problem(Elsevier, 2021) Shahmanzari, Masoud; Department of Business Administration; Aksen, Deniz; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 40308This paper presents a novel hybrid metaheuristic algorithm for the Roaming Salesman Problem (RSP), called Multi-Start Granular Skewed Variable Neighborhood Tabu Search (MS-GSVNTS). The objective in RSP is to design daily tours for a traveling campaigner who collects rewards from activities in cities during a fixed planning horizon. RSP exhibits a number of exclusive features: It is selective which implies that not every node needs a visit. The rewards of cities are time-dependent. Daily tours can be either an open or a closed tour which implies the absence of a fixed depot. Instead, there is a campaign base that is to be attended frequently. Multiple visits are allowed for certain cities. The proposed method MS-GSVNTS is tested on 45 real-life instances from Turkey which are built with actual travel distances and times and on 10 large scale instances. Computational results suggest that MS-GSVNTS is superior to the existing solution methods developed for RSP. It produces 50 best known solutions including 18 ties and 32 new ones. The performance of MS-GSVNTS can be attributed to its multi-start feature, rich neighborhood structures, skewed moves, and granular neighborhoods.Publication Metadata only A new gravity model with variable distance decay(Vilnius Gediminas Technical Univ Press, Technika, 2008) N/A; Department of Business Administration; Department of Business Administration; Sandıkçıoğlu, Müge; Ali, Özden Gür; Sayın, Serpil; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; N/A; 57780; 6755Our main goal is to understand the customers' store choice behavior in a grocery retail setting. We see this as a first vital step in order to make store location, format and product promotion decisions in the retail organization Proposed models in the literature generate consumer utility functions for different stores which are used in store sales estimation. For example, in one of its basic forms, Huff model proposes that, utility of a store for an individual is equal to the sales area of the store divided by a power of the individual's distance to the store. Parallel to this stream of research Multiplicative Competitor Interaction model estimates log-transformed utility functions by ordinary least squares regression. It is less specific in terms of variable selection compared to the Huff model. This paper proposes a new market share model which is a variant of the Huff model and evaluates most established market share models such as Huff and Multiplicative Competitor Interaction Model as well as a data mining method in a one-brand heterogonous size retail store setting. We observe that the Huff model performs well in its basic form. By representing distance decay value as a function of the sales area of the retail store we are able to improve the performance of the Huff model. We propose using optimization for estimating the model parameters in certain cases and observe that this improves the generalization ability of the model.Publication Metadata only Clustering grocery shopping paths of customers by using optimization-based models(Vilnius Gediminas Technical Univ Press, Technika, 2008) N/A; Department of Business Administration; Department of Industrial Engineering; Yaman, Tuğba; Karabatı, Selçuk; Karaesmen, Fikri; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; College of Engineering; N/A; 38819; 3579This study presents a preliminary investigation of shopping behavior of customers in a grocery store. Using each customer's in-store shopping path information, gathered by a wireless video camera that is affixed to the shopping cart, we classify customers into a predetermined number of clusters, and create a shopping path-based segmentation of customers. For this purpose a number of optimization models are developed. The results are presented in this paper. The next step is to analyze this collected data from different perspectives and developing different optimization models to achieve a better solution to the above clustering problem.Publication Metadata only Dynamic churn prediction framework with more effective use of rare event data: the case of private banking(Pergamon-Elsevier Science Ltd, 2014) Department of Business Administration; N/A; Ali, Özden Gür; Arıtürk, Umut; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Business; 57780; N/ACustomer churn prediction literature has been limited to modeling churn in the next (feasible) time period. On the other hand, lead time specific churn predictions can help businesses to allocate retention efforts across time, as well as customers, and identify early triggers and indicators of customer churn. We propose a dynamic churn prediction framework for generating training data from customer records, and leverage it for predicting customer churn within multiple horizons using standard classifiers. Further, we empirically evaluate the proposed approach in a case study about private banking customers in a European bank. The proposed framework includes customer observations from different time periods, and thus addresses the absolute rarity issue that is relevant for the most valuable customer segment of many companies. It also increases the sampling density in the training data and allows the models to generalize across behaviors in different time periods while incorporating the impact of the environmental drivers. As a result, this framework significantly increases the prediction accuracy across prediction horizons compared to the standard approach of one observation per customer; even when the standard approach is modified with oversampling to balance the data, or lags of customer behavior features are added as additional predictors. The proposed approach to dynamic churn prediction involves a set of independently trained horizon-specific binary classifiers that use the proposed dataset generation framework. In the absence of predictive dynamic churn models, we had to benchmark survival analysis which is used predominantly as a descriptive tool. The proposed method outperforms survival analysis in terms of predictive accuracy for all lead times, with a much lower variability. Further, unlike Cox regression, it provides horizon specific ranking of customers in terms of churn probability which allows allocation of retention efforts across customers and time periods. (C) 2014 Elsevier Ltd. All rights reserved.Publication Metadata only Optimal selection of energy efficiency measures for energy sustainability of existing buildings(Elsevier, 2016) Otay, Emre N.; Çamlıbel, Emre; Department of Business Administration; N/A; Tan, Barış; Yavuz, Yahya; Faculty Member; Master Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AThis study is motivated by the need to increase energy efficiency in existing buildings. Around 33% of the energy used in the world is consumed in the buildings. Identifying and investing in the right energy saving technologies within a given budget helps the adoption of energy efficiency measures in existing buildings. We use a mathematical programming approach to select the right energy efficiency measures among all the available ones to optimize financial or environmental benefits subject to budgetary and other logical constraints in single- and multi-period settings. We also present a business model to offer energy efficiency measures as a service. By using a real case study of a university campus, all the relevant energy efficiency measures are identified and their effects are determined by using engineering measurements and modelling. Through numerical experiments using the case data, we investigate and quantify the effects of using environmental or financial savings as the main objective, the magnitude of benefit of using a multi-period planning approach instead of a single-period approach, and also feasibility of offering energy saving technologies as a service. We show that substantial environmental and financial savings can be obtained by using the proposed method to select and invest in technologies in a multi-period setting. We also show that offering energy efficient technologies as a service can be a win-win-win arrangement for a service provider, its client, and also for the environment.Publication Metadata only SKU demand forecasting in the presence of promotions(Elsevier, 2009) van Woensel, Tom; Fransoo, Jan; Department of Business Administration; Department of Business Administration; Ali, Özden Gür; Sayın, Serpil; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 57780; 6755Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input features and model scope on an extensive SKU-store level sales and promotion time series from a European grocery retailer. At the high end of data and technique complexity, we propose using regression trees with explicit features constructed from sales and promotion time series of the focal and related SKU-store combinations. We observe that data pooling almost always improves model performance. The results indicate that simple time series techniques perform very well for periods without promotions. However, for periods with promotions, regression trees with explicit features improve accuracy substantially. More sophisticated input is only beneficial when advanced techniques are used. We believe that our approach and findings shed light into certain questions that arise while building a grocery sales forecasting system. (C) 2009 Elsevier Ltd. All rights reserved.