Researcher: Karaesmen, Fikri
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Karaesmen, Fikri
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Publication Metadata only Optimal threshold levels in stochastic fluid models via simulation-based optimization(Springer, 2007) Gurkan, Gul; Ozdemir, Ozge; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579A number of important problems in production and inventory control involve optimization of multiple threshold levels or hedging points. We address the problem of finding such levels in a stochastic system whose dynamics can be modelled using generalized semi-Markov processes (GSMP). The GSMP framework enables us to compute several performance measures and their sensitivities from a single simulation run for a general system with several states and fairly general state transitions. We then use a simulation-based optimization method, sample-path optimization, for finding optimal hedging points. We report numerical results for systems with more than twenty hedging points and service-level type probabilistic constraints. In these numerical studies, our method performed quite well on problems which are considered very difficult by current standards. Some applications falling into this framework include designing manufacturing flow controllers, using capacity options and subcontracting strategies, and coordinating production and marketing activities under demand uncertainty.Publication Metadata only Minimum-variance hedging for managing risks in inventory models with price fluctuations(Now Publishers, 2017) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Canyakmaz, Caner; Karaesmen, Fikri; Özekici, Süleyman; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 3579; 32631We consider the financial hedging of a random operational cash flow that arises in inventory operations with price and demand uncertainty. We use a variance minimization approach to find a financial portfolio that would minimize the total variance of operational and financial returns. For inventory models that involve continuous price fluctuations and price-dependent demand that arrives in continuous time, we characterize the minimum-variance hedging policies and numerically illustrate their effectiveness.Publication Metadata only The impact of retrials on call center performance(Springer, 2004) Aguir, Salah; Chauvet, Fabrice; Department of Industrial Engineering; Department of Business Administration; Karaesmen, Fikri; Karaesmen, Zeynep Akşin; Faculty Member; Faculty Member; Department of Industrial Engineering; Department of Business Administration; College of Engineering; College of Administrative Sciences and Economics; 3579; 4534This paper models a call center as a Markovian queue with multiple servers, where customer balking, impatience, and retrials are modeled explicitly. The resulting queue is analyzed both in a stationary and non-stationary setting. For the stationary setting a fluid approximation is proposed, which overcomes the computational burden of the continuous time markov chain analysis, and which is shown to provide an accurate representation of the system for large call centers with high system load. An insensitivity property of the retrial rate to key system parameters is established. The fluid approximation is shown to work equally well for the non-stationary setting with time varying arrival rates. Using the fluid approximation, the paper explores the retrial phenomenon for a real call center. The model is used to estimate the real arrival rates based on demand data where retrials cannot be distinguished from first time calls. This is a common problem encountered in call centers. Through numerical examples, it is shown that disregarding the retrial phenomenon in call centers can lead to huge distortions in subsequent forecasting and staffing analysis.Publication Metadata only Call center outsourcing contract analysis and choice(Informs, 2008) de Vericourt, Francis; Department of Business Administration; Department of Industrial Engineering; Karaesmen, Zeynep Akşin; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 4534; 3579This paper considers a call center outsourcing contract analysis and choice problem faced by a contractor and a service provider. The service provider receives an uncertain call volume over multiple periods and is considering outsourcing all or part of these calls to a contractor. Each call brings in a fixed revenue to the service provider. Answering calls requires having service capacity; thus implicit in the outsourcing decision is a capacity decision. Insufficient capacity implies that calls cannot be answered, which in turn means there will be a revenue loss. Faced, with a choice between a volume-based and a capacity-based contract offered by a contractor that has pricing power, the service provider determines optimal capacity levels. The optimal price and capacity of the contractor together with the optimal capacity of the service provider determine optimal profits of each party under the two contracts being considered. This paper characterizes optimal capacity levels and partially characterizes optimal pricing decisions under each contract. The impact of demand variability and the economic parameters on contract choice are explored through numerical examples. It is shown that no contract type is universally preferred and that operating environments as well as cost-revenue structures have an important effect.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 Stock rationing in an M/E-r/1 multi-class make-to-stock queue with backorders(Taylor & Francis, 2009) Gayon, Jean-Philippe; De Vericourt, Francis; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579A model of a single-item make-to-stock production system is presented. The item is demanded by several classes of customers arriving according to Poisson processes with different backorder costs. Item processing times have an Erlang distribution. It is shown that certain structural properties of optimal stock and capacity allocation policies exist for the case where production may be interrupted and restarted. Also, a complete characterization of the optimal policy in the case of uninterrupted production when excess production can be diverted to a salvage market is presented. A heuristic policy is developed and assessed based on the results obtained in the analysis. Finally the value of production status information and the effects of processing time variability are investigated.Publication Metadata only Pricing in a transportation station with strategic customers(Wiley, 2017) N/A; Department of Industrial Engineering; Department of Industrial Engineering; Department of Industrial Engineering; Manou, Athanasia; Canbolat, Pelin Gülşah; Karaesmen, Fikri; Faculty Member; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; College of Engineering; N/A; 108242; 3579We consider a transportation station, where customers arrive according to a Poisson process, observe the delay information and the fee imposed by the administrator and decide whether to use the facility or not. a transportation facility visits the station according to a renewal process and serves all present customers at each visit. We assume that every customer maximizes her individual expected utility and the administrator is a profit maximizer. We model this situation as a two-stage game among the customers and the administrator, where customer strategies depend on the level of delay information provided by the administrator. We consider three cases distinguished by the level of delay information: observable (the exact waiting time is announced), unobservable (no information is provided) and partially observable (the number of waiting customers is announced). in each case, we explore how the customer reward for service, the unit waiting cost, and the intervisit time distribution parameters affect the customer behavior and the fee imposed by the administrator. We then compare the three cases and show that the customers almost always prefer to know their exact waiting times whereas the administrator prefers to provide either no information or the exact waiting time depending on system parameters.Publication Metadata only Production/inventory control with advance demand information(Kluwer Academic Publishers, 2003) Liberopoulos, George; Dallery, Yves; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579Recent advances in information technology, such as EDI and web-based platforms, have made information exchange between supply chain partners cheaper and more secure. These advances also arrived at a time when the concepts of collaboration and partnership within supply chains are being recognized and developed. The principle premises of such concepts are rather simple and natural: more collaboration and more shared information should lead to better supply chain performance. The details, on the other hand, on how to achieve better performance through increased collaboration and information are not always trivial. This chapter focuses on the following particular issue regarding increased information exchange: how should advance demand information be used to increase performance in production/inventory systems and what is the extent of the performance increase that can be expected?Publication Metadata only Multi-product newsvendor problem with value-at-risk considerations(Elsevier Science Bv, 2009) Özler, Aysun; 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 the single period stochastic inventory (newsvendor) problem with downside risk constraints. The aim in the classical newsvendor problem is maximizing the expected profit. This formulation does not take into account the risk of earning less than a desired target profit or losing more than an acceptable level due to the randomness of demand. We utilize Value at Risk (VaR) as the risk measure in a newsvendor framework and investigate the multi-product newsvendor problem under a VaR constraint. To this end, we first derive the exact distribution function for the two-product newsvendor problem and develop an approximation method for the profit distribution of the N-product case (N>2). A mathematical programming approach is used to determine the solution of the newsvendor problem with a VaR constraint. This approach allows us to handle a wide range of cases including the correlated demand case that yields new results and insights. The accuracy of the approximation method and the effects of the system parameters on the Solution are investigated numerically.Publication Metadata only A revenue management problem with a choice model of consumer behaviour in a random environment(Springer, 2015) Ozkan, Can; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Fikri; Özekici, Süleyman; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 3579; 32631Modeling consumer behavior is a relevant and growing research area in revenue management. Single-resource (single-leg) capacity control problems comprising consumer choice modeling constitute the backbone of more complicated models. In existing models, the distribution of demand is assumed to be independent of external factors. However, in reality demand may depend on the current external environment which represents the prevailing economic, financial or other factors that affect customer behavior. We formulate a stochastic dynamic program that comprises a discrete choice model of consumer behavior in a randomly fluctuating demand environment with a Markovian structure. We derive some structural results on the optimal policy for capacity control. The model and the results generalize earlier work of Talluri and van Ryzin (Revenue management under a general discrete choice model of consumer behavior. Manag Sci 50(1):15-33 2004b). In particular, the concept of an efficient set of products plays an important rule but such sets may depend on the particular external environment. We also present some computational results which illustrate the structural properties and explore the benefits of explicitly modeling the external environment.