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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 How does self-concept clarity influence happiness in social settings? The role of strangers versus friends(Taylor _ Francis, 2018) Merdin-Uygur, Ezgi; Sarıal-Abi, Gülen; Hesapçı, Özlem; Department of Business Administration; Canlı, Zeynep Gürhan; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 16135Self-concept clarity (SCC), defined as the extent to which the content of an individual’s self-beliefs is clearly and confidently defined and internally consistent, influences experiences in social relationships. This paper extends the previous literature on SCC by proposing and demonstrating that high-SCC individuals anticipate and experience more happiness than low-SCC individuals when they share a social setting with friends and anticipate and experience less happiness than low-SCC individuals when they share a social setting with strangers and that this is because of perceived interpersonal distance. A series of four studies, including both online studies and a field study, support these predictions. Alternative explanations of self-esteem and self-efficacy are also ruled out. The findings yield both theoretical contributions and practical implications.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 Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes(Springer, 2019) Department of Business Administration; Department of Industrial Engineering; N/A; Tan, Barış; Dizbin, Nima Manafzadeh; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; Graduate School of Business; 28600; N/AEmpirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a base-stock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.Publication Open Access Purchasing, production, and sales strategies for a production system with limited capacity, fluctuating sales and purchasing prices(Taylor _ Francis, 2019) N/A; Department of Business Administration; Karabağ, Oktay; Tan, Barış; Resercher; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 28600In many industries, the revenue and cost structures of manufacturers are directly affected by the volatility of purchasing and sales prices in the markets. We analyze the purchasing, production, and sales policies for a continuous-review discrete material flow production/inventory system with fluctuating and correlated purchasing and sales prices, exponentially distributed raw material and demand inter-arrival times, and processing time. The sales and purchasing prices are driven by the random environmental changes that evolve according to a discrete state space continuous-time Markov process. We model the system as an infinite-horizon Markov decision process under the average reward criterion and prove that the optimal purchasing, production, and sales strategies are state-dependent threshold policies. We propose a linear programming formulation to compute the optimal threshold levels. We examine the effects of the sales price variation, purchasing price variation, correlation between sales and purchasing prices, customer arrival rate and limited inventory capacities on the system performance measures, through a range of numerical experiments. We also examine under which circumstances the use of the optimal policy notably improves the system profit compared to the use of the buy low and sell high naive policy. We show that using the optimal purchasing, production, and sales policies allow manufacturers to improve their profits when the purchasing and sales prices fluctuate.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 Base-rate information in consumer attributions of product-harm crises(American Marketing Association (AMA), 2012) Lei, Jing; Dawar, Niraj; Department of Business Administration; Department of Business Administration; Canlı, Zeynep Gürhan; Researcher; Department of Business Administration; College of Administrative Sciences and Economics; 16135Consumers spontaneously construct attributions for negative events such as product-harm crises. Base-rate information influences these attributions. The research findings suggest that for brands with positive prior beliefs, a high (vs. low) base rate of product-harm crises leads to less blame if the crisis is said to be similar to others in the industry (referred to as the "discounting effect"). However, in the absence of similarity information, a low (vs. high) base rate of crises leads to less blame toward the brand (referred to as the "subtyping effect"). For brands with negative prior beliefs, the extent of blame attributed to the brand is unaffected by the base-rate and similarity information. Importantly, the same base-rate information may have a different effect on the attribution of a subsequent crisis depending on whether discounting or subtyping occurred in the attribution of the first crisis. Consumers who discount a first crisis also tend to discount a second crisis for the same brand, whereas consumers who subtype a first crisis are unlikely to subtype again.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.