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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Production and energy mode control of a production-inventory system(Elsevier, 2023) Karabag, Oktay; Khayyati, Siamak; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsEnergy efficiency in manufacturing can be improved by controlling energy modes and production dy-namically. We examine a production-inventory system that can operate in Working, Idle, and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival, production and warm-up times, we formulate the problem of de-termining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy, inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival, production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint pro-duction and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary, we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system.Publication Metadata only Patient adherence in healthcare operations: a narrative review(Elsevier Ltd, 2024) Department of Business Administration; Kılıç, Hakan; Güneş, Evrim Didem; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and EconomicsPatient nonadherence to healthcare providers’ recommendations is a major obstacle to desired health outcomes. It results in health deterioration and hospitalization, which might have been avoided with a high level of patient adherence. This paper reviews the literature addressing the issue of adherence in healthcare operations. A total of 73 published articles from operations research and management science journals are included in the review as a result of the systematic search that covered studies from inception until October 26, 2022. This paper is the first comprehensive review of adherence-related research in the operations research field. We summarize how adherence is measured, the research contexts, interactions between adherence and the healthcare system, and how adherence is modeled mathematically. Furthermore, we review adherence-related research at clinical, hospital, and healthcare system levels of planning and control, in addition to medical decision-making. We identify the opportunities in adherence research under the following themes: Supporting proactive management of adherence for healthcare providers, designing a healthcare system that enables adherence, developing personalized treatments, and addressing the global health issues of antimicrobial resistance and vaccine hesitancy. © 2023Publication Metadata only Supported nondominated points as a representation of the nondominated set: an empirical analysis(Wiley, 2024) Department of Business Administration; Sayın, Serpil; Department of Business Administration; College of Administrative Sciences and EconomicsThe nondominated set of a multiple objective discrete optimization problem is known to contain unsupported nondominated points, which outnumber the supported ones and are more difficult to obtain. We treat supported nondominated points as a representation and analyse their quality using different metrics beyond their sheer numbers. Under different data generation schemes on multiobjective knapsack and assignment problems, we observe that supported nondominated points almost always provide a good representation of the entire nondominated set.Publication Metadata only Simultaneous control of multiple machines for energy efficiency: a simulation-based approach(Taylor and Francis, 2024) Frigerio, Nicla; Matta, Andrea; Department of Business Administration; Tan, Barış; Department of Business Administration; College of Administrative Sciences and EconomicsEnergy efficiency is crucial in contemporary industry and controlling the resource power state by switching off/on commands is a promising measure. The control problem of deciding when to switch off/on the machines depending on the state of the system at a given time is not trivial due to the effect the control might have on the system production rate. Threshold-based policies using buffer occupancy information to control the machines can be effectively used to reduce energy consumption. Nevertheless, highly complex control policies are difficult to be applied and costly to be managed in practice. Buffer-based threshold policies to control multiple machines simultaneously in a serial production line for energy efficiency purposes are analysed in this work. The optimal control minimises the energy consumption while assuring a certain target production rate for the system. The effects of controlling different combinations of machines simultaneously with different number of thresholds have been investigated through numerical experiments with discrete event simulation. Insights regarding the trade-off between the complexity of the control and the performance gains are provided. The proposed policy works effectively and the effect of a proper selection of the controlled machines or thresholds is significant.Publication Metadata only Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system(Elsevier B.V., 2025) Tan, Barış; Department of Business Administration; Özkan, Erhun; Department of Business Administration; College of Administrative Sciences and EconomicsWe study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server's energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments. © 2024Publication Metadata only Revenue management through dynamic cross selling in call centers(Wiley-Blackwell, 2010) N/A; Department of Industrial Engineering; Department of Business Administration; Örmeci, Lerzan; Karaesmen, Zeynep Akşin; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Business Administration; College of Engineering; College of Administrative Sciences and Economics; 32863; 4534This paper models the cross-selling problem of a call center as a dynamic service rate control problem. The question of when and to whom to cross sell is explored using this model. The analysis shows that, under the optimal policies, cross-selling targets may be a function of the operational system state. Sufficient conditions are established for the existence of preferred calls, i.e., calls that will always generate a cross-sell attempt. These provide guidelines in segment formation for marketing managers, and lead to a static heuristic policy. Numerical analysis establishes the value of different types of information, and different types of automation available for cross selling. Increased staffing for the same call volume is shown to have a positive and increasing return on revenue generation via cross selling, suggesting the need to staff for lower loads in call centers that aim to be revenue generators. The proposed heuristic leads to near optimal performance in a wide range of settings. 2010 Production and Operations Management Society.Publication Metadata only Flexibility structure and capacity design with human resource considerations(Wiley, 2015) Department of Business Administration; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Zeynep Akşin; Çakan, Nesrin; Karaesmen, Fikri; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 4534; N/A; 3579; 32863Most service systems consist of multidepartmental structures with multiskill agents that can deal with several types of service requests. The design of flexibility in terms of agents' skill sets and assignments of requests is a critical issue for such systems. The objective of this study was to identify preferred flexibility structures when demand is random and capacity is finite. We compare structures recommended by the flexibility literature to structures we observe in practice within call centers. To enable a comparison of flexibility structures under optimal capacity, the capacity optimization problem for this setting is formulated as a two-stage stochastic optimization problem. A simulation-based optimization procedure for this problem using sample-path gradient estimation is proposed and tested, and used in the subsequent comparison of the flexibility structures being studied. The analysis illustrates under what conditions on demand, cost, and human resource considerations, the structures found in practice are preferred.Publication Metadata only Price discrimination through multi-level loyalty programs(Springer, 2016) Department of Business Administration; Department of Economics; Sayman, Serdar; Usman, Ali Murat; Faculty Member; Teaching Faculty; Department of Business Administration; Department of Economics; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 112222; 100999Loyalty programs often feature multiple rewards with different requirements; for instance, an airline offering a free domestic ticket for 10 K miles, and an international ticket for 20 K miles. This research focuses on the role of multi-level rewards as a segmentation and price discrimination mechanism: Multi-level rewards can increase firm profits when buyers differ in purchase frequency and/or time discount factor. We propose that a program with two rewards can be designed in such a way that (i) it is more profitable than a one-reward program, and (ii) buyers self-select. Light users prefer to receive the smaller reward two times over receiving the larger reward one time, even though the smaller reward is less than half of the larger reward. We show that the smaller reward helps the firm enlarge its base in the light user segment. We also compare multi-level programs with quantity discounts.Publication Metadata only Joint dynamic pricing of multiple perishable products under consumer choice(The Institute for Operations Research and the Management Sciences (INFORMS), 2010) Natarajan, Harihara Prasad; Xu, Susan H.; Department of Business Administration; Akçay, Yalçın; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 51400In response to competitive pressures, firms are increasingly adopting revenue management opportunities afforded by advances in information and communication technologies. Motivated by these revenue management initiatives in industry, we consider a dynamic pricing problem facing a firm that sells given initial inventories of multiple substitutable and perishable products over a finite selling horizon. Because the products are substitutable, individual product demands are linked through consumer choice processes. Hence, the seller must formulate a joint dynamic pricing strategy while explicitly incorporating consumer behavior. For an integrative model of consumer choice based on linear random consumer utilities, we model this multiproduct dynamic pricing problem as a stochastic dynamic program and analyze its optimal prices. The consumer choice model allows us to capture the linkage between product differentiation and consumer choice, and readily specializes to the cases of vertically and horizontally differentiated assortments. When products are vertically differentiated, our results show monotonicity properties (with respect to quality, inventory, and time) of the optimal prices and reveal that the optimal price of a product depends on higher quality product inventories only through their aggregate inventory rather than individual availabilities. Furthermore, we show that the price of a product can be decomposed into the price of its adjacent lower quality product and a markup over this price, with the markup depending solely on the aggregate inventory. We exploit these properties to develop a polynomial-time, exact algorithm for determining the optimal prices and the profit. For a horizontally differentiated assortment, we show that the profit function is unimodal in prices. We also show that individual, rather than aggregate, product inventory availability drives pricing. However, we find that monotonicity properties observed in vertically differentiated assortments do not hold.