Researcher: Örmeci, Lerzan
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Örmeci, Lerzan
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Publication Metadata only A preference-based appointment scheduling problem with multiple patient types(TMMOB Makine Mühendisleri Odası, 2019) N/A; Department of Industrial Engineering; Tunçalp, Feray; Örmeci, Lerzan; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 32863This paper focuses on the appointment scheduling mechanism of a physician or a diagnostic resource in a healthcare facility. Multiple patient types with different revenues use the facility. The facility observes the number of appointment requests arriving from each patient type at the beginning of each day. It decides on how to allocate available appointment slots to these appointment requests. Patients prefer a day in the booking horizon with a specific probability and they have only one preference. Patients are either given an appointment for their preferred days or their appointment requests are rejected. The facility wants to keep the rejection costs at a certain level, while maximizing its revenues. This process is modeled with a discrete time and constrained Markov Decision Process to maximize the infinitehorizon expected discounted revenue. The constraint guarantees that the infinite-horizon expected discounted rejection cost is below a specific threshold. We have proved that the optimal policy is a randomized booking limit policy. To solve the model, we have implemented Temporal Difference (TD) Learning Algorithm, which is a well-known Approximate Dynamic Programming (ADP) method. We have compared the ADP results with other heuristics numerically / Bu makale, bir sağlık tesisindeki bir doktor ya da tanı cihazının randevu planlama mekanizmasına odaklanmaktadır. Bu tesisi, getirileri birbirinden farklı olan birden çok hasta tipi kullanmaktadır. Tesis, her hasta tipinden gelen randevu isteklerini her günün başında gözlemlemektedir. Müsait randevu saatlerini bu randevu isteklerine nasıl tahsis edeceğine karar vermektedir. Hastalar belli bir olasılıkla rezervasyon dönemindeki bir günü tercih etmektedirler ve sadece bir tercihleri vardır. Hastalara ya tercih ettiği güne bir randevu verilmektedir ya da randevu istekleri reddedilmektedir. Tesis, getirilerini maksimize ederken reddedilme maliyetlerini belli bir seviyede tutmak istemektedir. Bu süreç, sonsuz zamanlı beklenen indirgenmiş karı maksimize etmek için ayrık zamanlı ve kısıtlı Markov Karar Süreci ile modellenmektedir. Kısıt, sonsuz zamanlı beklenen indirgenmiş reddedilme maliyetlerinin belli bir eşik değerinin altında olmasını garanti etmektedir. En iyi politikanın rassallaştırılmış bir rezervasyon limiti politikasının olduğunu gösterdik. Modeli çözmek için iyi bilinen bir “Yaklaşık Dinamik Programlama” metodu olan “Geçici Farklarla Öğrenme Algoritmasını” uyguladık. “Yaklaşık Dinamik Programlama” sonuçlarını diğer buluşsal yöntemlerle sayısal olarak karşılaştırdık.Publication Metadata only The 39th international conference of the EURO working group on operational research applied to health services: ORAHS 2013 special issue(Springer, 2015) Çayırlı, Tuğba; Günal, Murat M.; Department of Business Administration; Department of Industrial Engineering; Güneş, Evrim Didem; Örmeci, Lerzan; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 51391; 32863N/APublication Metadata only Admission control with batch arrivals(Elsevier Science Bv, 2004) Burnetas, A; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863We consider the problem of dynamic admission control in a multi-class Markovian loss system receiving random batches, where each admitted class-i job demands an exponential service with rate mu, and brings a reward r(i). We show that the optimal admission policy is a sequential threshold policy with monotone thresholds.Publication Metadata only Stochastic models for the coordinated production and shipment problem in a supply chain(Pergamon-Elsevier Science Ltd, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a stochastic environment. The supplier controls the production, holds inventory and ships the products to the retailer to satisfy the external demand. We model the system as a Markov decision process, and show that the optimal production and transportation decisions are complex and non-monotonic. Therefore, we analyze two widely-used shipment policies in the industry as well, namely time-based and quantity-based shipment policies in addition to a hybrid time-and-quantity based shipment policy. We numerically compare the performances of these policies with respect to the optimal policy and analyze the effects of the parameters in the system.Publication Metadata only A dynamic inventory rationing problem with uncertain demand and production rates(Springer, 2015) Turgay, Zeynep; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Fikri; Örmeci, Lerzan; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 3579; 32863We investigate the structural properties of a finite horizon, discrete time single product inventory rationing problem, where we allow random replenishment (production) opportunities. In contrast to the standard models of dynamic capacity control in revenue management or production/inventory systems, we assume that the demand/production rates are not known with certainty but lie in some interval. To address this uncertainty, we formulate a robust stochastic dynamic program and show how the structural properties of the optimal policy propagate to the robust counterpart of the problem. Further, we explore how the optimal policy changes with respect to the uncertainty set. We also show that our results can be extended to certain alternative robust formulations.Publication Metadata only A coordinated production and shipment model in a supply chain(Elsevier Science Bv, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a deterministic inventory system. In this supply chain, the customers are willing to wait at the expense of a waiting cost. Accordingly, the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time. The supplier produces the items, holds the inventory and ships the products to the retailer to satisfy the external demand. We investigate both a coordinated production/transportation model and a decentralized model. In the decentralized model, the retailer manages his own system and sends orders to the supplier, while the supplier determines her own production process and the amount to produce in an inventory replenishment cycle according to the order quantity of the retailer. However, in the coordinated model, the supplier makes all the decisions, so that she determines the length of the replenishment and transportation cycles as well as the shipment quantities to the retailer. We determine the structure of the optimal replenishment and transportation cycles hi both coordinated and decentralized models and the corresponding costs. Our computational results compare the optimal costs under the coordinated and decentralized models. We also numerically investigate the effects of several parameters on the optimal solutions.Publication Metadata only A modeling framework for control of preventive services(Informs, 2016) Kunduzcu, Derya; Department of Industrial Engineering; Department of Business Administration; Örmeci, Lerzan; Güneş, Evrim Didem; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Business Administration; College of Engineering; College of Administrative Sciences and Economics; 32863; 51391We present a modeling framework for facilities that provide both screening (preventive) and diagnostic (repair) services. The facility operates in a random environment that represents the condition of the population that needs screening and diagnostic services, such as the disease prevalence level. We model the environment as a partially endogenous process: the population's health can be improved by providing screening services, which reduces future demand for diagnostic services. We use event-based dynamic programming to build a framework for modeling different kinds of these facilities. This framework contains a number of service priority policies that are concerned with prioritizing screening versus diagnostic services. The main trade-off is between serving urgent diagnostic needs and providing screening services that may decrease future diagnostic needs. Under certain conditions, this trade-off reverses the famous c,u, rule; i.e., the patients with lower waiting cost are given priority over the others. We define appropriate event operators and specify the properties preserved by these operators. These characterize the structure of optimal policies for all models that can be built within this framework. A numerical study on colonoscopy services illustrates how the framework can be used to gain insights on developing good screening policies.Publication Metadata only Join, balk, or jettison? the effect of flexibility and ranking knowledge in systems with batch arrivals(Wiley, 2022) Bountali, Olga; Burnetas, Apostolos; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863Families that visit theme parks like Disneyland are debating on two aspects when they try to determine whether they prefer to join an activity of interest or would rather balk: (1) Is it better to join or balk as a group or allow the flexibility to get separated and jettison some members? and (2) Will it make any difference if they set a ranking among themselves beforehand as to who will be served first, second, etc.? We tackle the effect of flexibility and ranking knowledge and answer the above questions considering a single server Markovian queue with a generic batch size distribution. We consider two levels of flexibility: an inflexible setting, under which a family makes a common decision, and a flexible setting, under which each member makes her own decision. We pair each level with two sublevels with respect to the ranking knowledge: the case where the members set their ranking beforehand, and the case where they do not and assume they will be served according to a random order. We provide a full analytical characterization of the equilibrium and socially optimal strategies, and a comprehensive analysis of the intricate interplay among flexibility, ranking knowledge, and batch size variability, notions that do not exist in single-ins arrival systems. We offer insights as to under which circumstances entity jettison is preferable. We investigate the corresponding implications of the above on system throughput and social welfare and determine which setting is preferable for the customers and which for the society, depending on the objective and the system dynamics. Further, we highlight key differences between single versus batch-arrival models and provide high-level guidelines for managers and policymakers as to how they can influence customer decisions so that they move toward the preferable setting (e.g., by revealing/concealing the ranking, encouraging flexibility, pricing, etc.).Publication Metadata only Optimizing specimen collection for processing in clinical testing laboratories(Elsevier, 2013) Gel, Esma S.; Gel, Aytekin; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Yücel, Eda; Salman, Fatma Sibel; Örmeci, Lerzan; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 235501; 178838; 32863We study the logistics of specimen collection for a clinical testing laboratory that serves sites dispersed in an urban area. The specimens that accumulate at the customer sites throughout the working day are transported to the laboratory for processing. The problem is to construct and schedule a series of tours to collect the accumulated specimens from the sites throughout the day. Two hierarchical objectives are considered: (i) maximizing the amount of specimens processed by the next morning, and (ii) minimizing the daily transportation cost. We show that the problem is NP-hard and formulate a linear Mixed Integer Programming (MIP) model to solve the bicriteria problem in two levels. We characterize properties of optimal solutions and develop a heuristic approach based on solving the MIP model with additional constraints that seeks for feasible solutions with specific characteristics. To evaluate the performance of this approach, we provide an upper bounding scheme on the daily processed amount, and develop two relaxed MIP models to generate lower bounds on the daily transportation cost. The effectiveness of the proposed solution approach is evaluated using realistic problem instances. Insights on key problem parameters and their effects on the solutions are extracted by further experiments.Publication Metadata only Dynamic policies of admission to a two-class system based on customer offers(Taylor & Francis, 2002) Burnetas, A.N.; Emmons, H.; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863We consider the problem of dynamic admission control in a Markovian loss queueing system with two classes of jobs with different service rates and random revenues. We establish the existence of an optimal monotone policy. We also show that under certain conditions there exist preferred jobs from either class.