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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access An investigation of time-inconsistency(Informs, 2009) Öncüler, Ayşe; Department of Business Administration; Department of Business Administration; Sayman, Serdar; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 112222Preference between two future outcomes may change over time-a phenomenon labeled as time inconsistency. The term "time inconsistency" is usually used to refer to cases in which a larger-later outcome is preferred over a smaller-sooner one when both are delayed by some time, but then with the passage of time a preference switches to the smaller-sooner outcome. The current paper presents four empirical studies showing that time inconsistency in the other direction is also possible: A person may prefer the smaller-sooner outcome when both options are in the future, but decide to wait for the larger-later one when the smaller option becomes immediately available. We. find that such "reverse time inconsistency" is more likely to be observed when the delays to and between the two outcomes are short (up to a week). We propose that reverse time inconsistency may be associated with a reversed-S shape discount function, and provide evidence that such a discount function captures part of the variation in intertemporal preferences.Publication Open Access Data-driven control of a production system by using marking-dependent threshold policy(Elsevier, 2020) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/AAs increasingly more shop-floor data becomes available, the performance of a production system can be improved by developing effective data-driven control methods that utilize this information. We focus on the following research questions: how can the decision to produce or not to produce at any time be given depending on the real-time information about a production system?; how can the collected data be used directly in optimizing the policy parameters?; and what is the effect of using different information sources on the performance of the system? In order to answer these questions, a production/inventory system that consists of a production stage that produces to stock to meet random demand is considered. The system is not fully observable but partial production and demand information, referred to as markings is available. We propose using the marking-dependent threshold policy to decide whether to produce or not based on the observed markings in addition to the inventory and production status at any given time. An analytical method that uses a matrix geometric approach is developed to analyze a production system controlled with the marking-dependent threshold policy when the production, demand, and information arrivals are modeled as Marked Markovian Arrival Processes. A mixed integer programming formulation is presented to determine the optimal thresholds. Then a mathematical programming formulation that uses the real-time shop floor data for joint simulation and optimization (JSO) of the system is presented. Using numerical experiments, we compare the performance of the JSO approach to the analytical solutions. We show that using the marking-dependent control policy where the policy parameters are determined from the data works effectively as a data-driven control method for manufacturing.Publication Open Access United we stand: the impact of buying groups on retailer productivity(American Marketing Association (AMA), 2015) Geyskens, Inge; Gielens, Katrijn; Department of Business Administration; Wuyts, Stefan; Faculty Member; Department of Business Administration; College of Administrative Sciences and EconomicsIn diverse industries, from grocery retailing to health care, retailers join buying groups to achieve better terms with suppliers. The authors track the buying group membership of Europe's largest grocery retailers over a 15-year period and evaluate why some buying groups are better than others in increasing retailer performance and why different members belonging to the same group do not always benefit equally from their membership. They find that, on average, buying groups indeed generate scale advantages for their members: group scale increases group members' productivity and sales and decreases their cost of goods sold. Still, bigger is not always better. Retailers benefit less from buying group scale when the group is more heterogeneous in terms of member size and when it extends its scope across too many markets. Moreover, the smaller a member is within the group and the more it overlaps with fellow members, the less it benefits.Publication Open Access Optimal control of production-inventory systems with correlated demand inter-arrival and processing times(Elsevier, 2020) Department of Business Administration; N/A; Tan, Barış; Faculty Member; Department of Business Administration; Graduate School of Business; College of Administrative Sciences and Economics; College of Engineering; N/A; 28600We consider the production control problem of a production-inventory system with correlated demand inter-arrival and processing times that are modeled as Markovian Arrival Processes. The control problem is minimizing the expected average cost of the system in the steady-state by controlling when to produce an available part. We prove that the optimal control policy is the state-dependent threshold policy. We evaluate the performance of the system controlled by the state-dependent threshold policy by using the Matrix Geometric method. We determine the optimal threshold levels of the system by using policy iteration. We then investigate how the autocorrelation of the arrival and service processes impact the performance of the system. Finally, we compare the performance of the optimal policy with 3 benchmark policies: a state-dependent policy that uses the distribution of the inter-event times but assumes i.i.d.inter-event times, a single-threshold policy that uses both the distribution and also the autocorrelation, and a single-threshold policy that uses the distribution of the inter-event times but assumes they are not correlated. Our analysis demonstrates that ignoring autocorrelation in setting the parameters of the production policy causes significant errors in the expected inventory and backlog costs. A single-threshold policy that sets the threshold based on the distribution and also the autocorrelation performs satisfactorily for systems with negative autocorrelation. However, ignoring positive correlation yields high errors for the total cost. Our study shows that an effective production control policy must take correlations in service and demand processes into account.Publication Open Access Production control of a pull system with production and demand uncertainty(Institute of Electrical and Electronics Engineers (IEEE), 2002) Department of Business Administration; Tan, Barış; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600We consider a continuous material-flow manufacturing system with an unreliable production system and a variable demand source which switches randomly between zero and a maximum level. The failure and repair times of the production system and the switching times of the demand source are assumed to be exponentially distributed random variables. The optimal production flow control policy that minimizes the expected average inventory carrying and backlog costs is characterized as a double-hedging policy. The optimal hedging levels are determined analytically by minimizing the closed-form expression of the cost function. We investigate two approximate single hedging policies. It is empirically shown that an approximate policy that uses a single hedging level which is the sum of a production uncertainty term and a demand uncertainty term gives accurate results for the expected average cost.Publication Open Access Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: a case study(Elsevier, 2023) Bozkır, C.D.C.; Özmemiş, C.; Kurbanzade, A.K.; Balçık, B.; Tuğlular, S.; Department of Business Administration; Güneş, Evrim Didem; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51391Planning treatments of different types of patients have become challenging in hemodialysis clinics during the COVID-19 pandemic due to increased demands and uncertainties. In this study, we address capacity planning decisions of a hemodialysis clinic, located within a major public hospital in Istanbul, which serves both infected and uninfected patients during the COVID-19 pandemic with limited resources (i.e., dialysis machines). The clinic currently applies a 3-unit cohorting strategy to treat different types of patients (i.e., uninfected, infected, suspected) in separate units and at different times to mitigate the risk of infection spread risk. Accordingly, at the beginning of each week, the clinic needs to allocate the available dialysis machines to each unit that serves different patient cohorts. However, given the uncertainties in the number of different types of patients that will need dialysis each day, it is a challenge to determine which capacity configuration would minimize the overlapping treatment sessions of different cohorts over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the hemodialysis clinic to illustrate the effectiveness of the proposed model. We also compare the performance of different cohorting strategies with three and two patient cohorts.Publication Open Access Modeling and analysis of an auction-based logistics market(Elsevier, 2008) Ağralı, Semra; Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; 3579We consider a logistics spot market where the transportation orders from a number of firms are matched with two types of carriers through a reverse auction. In the spot market, local carriers compete with in-transit carriers that have lower costs. In order to analyze the effects of implementing a logistics spot market on these three parties: firms, local carriers, and in-transit carriers and also the effects of various system parameters, we develop a two-stage stochastic model. We first model the auction in a static setting and determine the expected auction price based on the number of carriers engaging in the auction and their cost distributions. We then develop a continuous-time Markov chain model to evaluate the performance of the system in a dynamic setting with random arrivals and possible abandonment of orders and carriers. By combining these two models, we evaluate the performance measures such as the expected auction price, price paid to the carriers, distribution of orders between local and in-transit carriers, and expected number of carriers and orders waiting at the logistics center in the long run. We present analytical and computational results related to the performance of the system and discuss operation of such a logistics spot market in Turkey.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 Open Access Front-office multitasking between service encounters and back-office tasks(Elsevier, 2020) Legros, Benjamin; Jouini, Oualid; Koole, Ger; Department of Business Administration; Karaesmen, Zeynep Akşin; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 4534We model the work of a front-line service worker as a queueing system. The server interacts with customers in a multi-stage process with random durations. Some stages require an interaction between server and customer, while other stages are performed by the customer as a self-service task or with the help of another resource. Random arrivals by customers at the beginning and during an encounter create random lengths of idle time in the work of the server (breaks and interludes respectively). The server considers treatment of an infinite amount of back-office tasks, or tasks that do not require interaction with the customer, during these idle times. We consider an optimal control problem for the server's work. The main question we explore is whether to use the interludes in service encounters for treating back-office, when the latter incur switching times. Under certain operating environments, working on back-office during interludes is shown to be valuable. Switching times play a critical role in the optimal control of the server's work, at times leading the server to prefer remaining idle during breaks and interludes, instead of working on back-office, and at others to continue back-office in the presence of waiting customers. The optimal policy for use of the interludes is one with multiple thresholds depending on both the customers queueing for service, and the ones who are in-service. We illustrate that in settings with multiple interludes in an encounter, if at all, the back-office work should be concentrated on fewer, longer and later interludes.Publication Open Access Design of balanced energy savings performance contracts(Taylor _ Francis, 2020) Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600Energy savings performance contracts between the energy users and the energy service companies (ESCO) are used to finance energy efficiency investments by using the future energy savings that will result from these investments. We present an analytical model to characterise the energy savings performance contracts and discuss how the risks of estimating the energy savings affect the energy user and the service provider. This characterisation allows determination of the contract parameters for a balanced contract with the information about the energy savings that are expected from the planned energy-efficiency investments. Since it is difficult to get the statistical information about the energy savings before investing in an energy-efficiency project, we develop a distribution-free contract that sets the guaranteed energy savings level based on the mean and the standard deviation of the energy savings and the profit-sharing ratio between the ESCO and the energy user. We show that a simple distribution-free balanced contract performs satisfactorily when the distribution of the energy savings is not known and its mean and the standard deviation are estimated with error. Our analytical results show that the energy savings contracts with the right parameters can mitigate the risks related to realisation of the anticipated energy savings.