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Publication Open Access A method for estimating stock-out-based substitution rates by using point-of-sale data(Taylor _ Francis, 2009) Öztürk, Ömer Cem; Department of Business Administration; Tan, Barış; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600; 38819Empirical studies in retailing suggest that stock-out rates are quite high in many product categories. Stock-outs result in demand spillover, or substitution, among items within a product category. Product assortment and inventory management decisions can be improved when the substitution rates are known. In this paper, a method is presented to estimate product substitution rates by using only Point-Of-Sale (POS) data. The approach clusters POS intervals into states where each state corresponds to a specific substitution scenario. Then available POS data for each state is consolidated and the substitution rates are estimated using the consolidated information. An extensive computational analysis of the proposed substitution rate estimation method is provided. The computational analysis and comparisons with an estimation method from the literature show that the proposed estimation method performs satisfactorily with limited information.
Publication 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 near-optimal order-based inventory allocation rule in an assemble-to-order system and its applications to resource allocation problems(Springer, 2005) Xu, Susan Hong; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400Assemble-to-order (ATO) manufacturing strategy has taken over the more traditional make-to-stock (MTS) strategy in many high-tech firms. ATO strategy has enabled these firms to deliver customized demand timely and to benefit from risk pooling due to component commonality. However, multi-component, multi-product ATO systems pose challenging inventory management problems. In this chapter, we study the component allocation problem given a specific replenishment policy and realized customer demands. We model the problem as a general multidimensional knapsack problem (MDKP) and propose the primal effective capacity heuristic (PECH) as an effective and simple approximate solution procedure for this NP-hard problem. Although the heuristic is primarily designed for the component allocation problem in an ATO system, we suggest that it is a general solution method for a wide range of resource allocation problems. We demonstrate the effectiveness of the heuristic through an extensive computational study which covers problems from the literature as well as randomly generated instances of the general and 0-1 MDKP. In our study, we compare the performance of the heuristic with other approximate solution procedures from the ATO system and integer programming literature.Publication Open Access A preference-based, multi-unit auction for pricing and capacity allocation(Elsevier, 2018) Lessan, Javad; Department of Business Administration; Karabatı, Selçuk; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 38819We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint.Publication Metadata only A stochastic model of vessel casualties resulting from oil tanker traffic through narrow waterways(Soc Computer Simulation, 1998) Otay, Emre; Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600In this paper, we present our preliminary results of a stochastic model to investigate vessel casualties resulting from tanker traffic through a waterway. A state-space model of the waterway is developed by dividing it into a number of grids. The model incorporates the effects of physical forcing mechanisms, i.e., currents and waves, winds, visibility, geometry of the waterway and the routes of individual vessels in the drift probabilities of the vessels. Then these probabilities are used as state-transition probabilities of a Markov chain. The transient analysis of the resulting time-varying Markov chain yields risk charts that show the casualty probabilities across the geometry of the waterway at a given time. Furthermore the steady-state analysis allows us to analyze the relationship between the vessel traffic intensity and a global measure of casualty risk.Publication Open Access Bilevel programming for generating discrete representations in multiobjective optimization(Springer, 2018) Kirlik, Gökhan; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755The solution to a multiobjective optimization problem consists of the nondominated set that portrays all relevant trade-off information. The ultimate goal is to identify a Decision Maker's most preferred solution without generating the entire set of nondominated solutions. We propose a bilevel programming formulation that can be used to this end. The bilevel program is capable of delivering an efficient solution that maps into a given set, provided that one exits. If the Decision Maker's preferences are known a priori, they can be used to specify the given set. Alternatively, we propose a method to obtain a representation of the nondominated set when the Decision Maker's preferences are not available. This requires a thorough search of the outcome space. The search can be facilitated by a partitioning scheme similar to the ones used in global optimization. Since the bilevel programming formulation either finds a nondominated solution in a given partition element or determines that there is none, a representation with a specified coverage error level can be found in a finite number of iterations. While building a discrete representation, the algorithm also generates an approximation of the nondominated set within the specified error factor. We illustrate the algorithm on the multiobjective linear programming 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 Evaluating average and heterogeneous treatment effects in light of domain knowledge: impact of behaviors on disease prevalence(Ieee, 2019) N/A; Department of Business Administration; Ghanem, Angi Nazih; Ali, Özden Gür; N/A; Faculty Member; Department of Business Administration; N/A; College of Administrative Sciences and Economics; N/A; 57780Understanding causal treatment effect and its heterogeneity can improve targeting of efforts for prevention and treatment of diseases. A number of methods are emerging to estimate heterogeneous treatment effect from observational data, such as Causal Forest. In this paper, we evaluate the heterogeneous treatment effect estimates in terms of whether they recover the expected direction of the effect based on domain knowledge. We use the individual level health surveys conducted by the Turkish Statistical Institute (TUIK) over the span of eight years with 90K+ observations. We estimate the effect of six behaviors on the probability of two diseases (IHD and Diabetes). We compare two approaches: a) treatment and disease specific Causal Forest models that directly estimate the heterogeneous treatment effect, and b) disease specific Random Forest models of disease probability that are used as simulators to evaluate counterfactual scenarios. We find that, with some exceptions, the signs of Causal Forest heterogeneous treatment effects are aligned with domain knowledge. Causal Forest performed better than the more naive approach of using RF models as simulators which disregards selection bias in treatment assignment.Publication Open Access Managing manufacturing risks by using capacity options(Springer, 2002) Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600In this study, we investigate the strategy of increasing production capacity temporarily through contingent contractual agreements with short-cycle manufacturers to manage the risks associated with demand volatility. We view all these agreements as capacity options. More specifically, we consider a manufacturing company that produces a replenishment product that is sold at a retailer. The demand for the product switches randomly between a high level and a low level. The production system has enough capacity to meet the demand in the long run. However, when the demand is high, it does not have enough capacity to meet the instantaneous demand and thus has to produce to stock in advance. Alternatively, a contractual agreement with a short-cycle manufacturer can be made. This option gives the right to receive additional production capacity when needed. There is a fixed cost to purchase this option for a period of time and, if the option is exercised, there is an additional per unit exercise price which corresponds to the cost of the goods produced at the short-cycle manufacturer. We formulate the problem as a stochastic optimal control problem and analyse it analytically. By comparing the costs between two cases where the contract with the short-cycle manufacturer is used or not, the value of this option is evaluated. Furthermore, the effect of demand variability on this contract is investigated.Publication Metadata only Modeling and analysis of a cooperative service network(Pergamon-Elsevier Science Ltd, 2021) N/A; N/A; Department of Business Administration; Hosseini, Behnaz; Tan, Barış; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600With the advances in technology and changes in customers' attitude towards different service delivery formats, it is important for the service providers to deliver online services in addition to the traditional face-to-face services. In the cooperative service network presented in this study, service providers cooperate to serve online service requests received by the network in addition to their own customers. Designing and managing the cooperative network effectively increase the utilization of the involved servers, provide an adequate service for the external customers, and increase the profit for both the network and service providers. From the operational perspective, the number and utilization of the members to be included in the network and the price that will be paid to each member for a directed request are the main design questions. In order to answer these questions, we present a stochastic model that captures the dynamics of customer arrivals, assignment, and admission control. To establish this model, we first derive the solution of the dynamic admission control problem for the servers who decide how to admit their own customers and the external online customers using a Markov decision process. We then analyze the operation of the whole network with the servers who use the optimal admission control policy and obtain the system performance measures depending on the members' operational parameters. These results are used to determine the optimal number of servers in the network and the service price to be paid to the participating servers in order to maximize the obtained profit. We show that a cooperative service network is an effective way of utilizing the idle capacity of the servers while providing an adequate service level for the external online customers and increasing the profit for both the network and service providers.