Person: KUNDAKCIOĞLU, Ömer Erhun
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Ömer Erhun
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KUNDAKCIOĞLU
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ArticlePublication Metadata only Healthcare intelligence: Turning data into knowledge(IEEE, 2014) Yang, H.; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer ErhunExceptional opportunities exist for researchers and practitioners to invest in conducting innovative and transformative research in data mining and health informatics. This IEEE Intelligent Systems "Trends and Controversies" (T&C) department hopes to raise awareness and highlight recent research to move toward such goals. The introduction, "Healthcare Intelligence: Turning Data into Knowledge," is written by Hui Yang and Erhun Kundakcioglu. Next, "Empowering Excellence of Care by Radiology Informatics" is written by Jing Li, Teresa Wu, J. Ross Mitchell, Amy K. Hara, William Pavlicek, Leland S. Hu, Alvin C. Silva, and Christine M. Zwart. Third, "Opportunities for Operations Research in Medical Decision Making" is written by Sait Tunc, Oguzhan Alagoz, and Elizabeth Burnside. Fourth, "Diagnostic Network Modeling of Neural Connectivity Using Functional Magnetic Resonance Imaging" is written by W. Art Chaovalitwongse, Georgiy Presnyakov, Yulian Cao, Sirirat Sujitnapitsatham, Daehan Won, Tara Madhyastha, Kurt E. Weaver, Paul R. Borghesani, and Thomas J. Grabowski. The final article, "Spatial Clustering in Public Health: Advances and Challenges," is written by Lianjie Shu, Man Ho Ling, Shui-Yee Wong, and Kwok-Leung Tsui.ArticlePublication Metadata only An EOQ model with deteriorating items and self-selection constraints(Springer Nature, 2020-09) Önal, Mehmet; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; ÖNAL, MehmetIn this paper, we consider a store that sells two vertically differentiated items that might substitute each other. These items do not only differ in quality and price, but they also target two different customer segments. Items deteriorate over time and might require price adjustments to avoidcannibalization. We provide closed-form solutions for pricing and ordering of these items that lead to key managerial insights.ArticlePublication Metadata only Multi-instance learning by maximizing the area under receiver operating characteristic curve(Springer, 2023-02) Sakarya, I. E.; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer ErhunThe purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances.ArticlePublication Metadata only Exact and heuristic approaches to detect failures in failed k-out-of-n systems(Elsevier, 2019-12) Yavuz, Tonguç; Kundakcıoğlu, Ömer Erhun; Ünlüyurt, T.; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Yavuz, TonguçThis paper considers a k-out-of-n system that has just failed. There is an associated cost of testing each component. In addition, we have apriori information regarding the probabilities that a certain set of components is the reason for the failure. The goal is to identify the subset of components that have caused the failure with the minimum expected cost. In this work, we provide exact and approximate policies that detects components' states in a failed k-out-of-n system. We propose two integer programming (IP) formulations, two novel Markov decision process (MDP) based approaches, and two heuristic algorithms. We show the limitations of exact algorithms and effectiveness of proposed heuristic approaches on a set of randomly generated test instances. Despite longer CPU times, IP formulations are flexible in incorporating further restrictions such as test precedence relationships, if need be. Numerical results illustrate that dynamic programming for the proposed MDP model is the most effective exact method, solving up to 12 components within one hour. The heuristic algorithms' performances are presented against exact approaches for small to medium sized instances and against a lower bound for larger instances.ArticlePublication Metadata only A model to estimate cost-savings in diabetic foot ulcer prevention efforts(Elsevier, 2017) Barshes, N. R.; Saedi, S.; Wrobel, J.; Kougias, P.; Kundakcıoğlu, Ömer Erhun; Armstrong, D. G.; Industrial Engineering; KUNDAKCIOĞLU, Ömer ErhunBackground: Sustained efforts at preventing diabetic foot ulcers (DFUs) and subsequent leg amputations are sporadic in most health care systems despite the high costs associated with such complications. We sought to estimate effectiveness targets at which cost-savings (i.e. improved health outcomes at decreased total costs) might occur. Methods: A Markov model with probabilistic sensitivity analyses was used to simulate the five-year survival, incidence of foot complications, and total health care costs in a hypothetical population of 100,000 people with diabetes. Clinical event and cost estimates were obtained from previously-published trials and studies. A population without previous DFU but with 17% neuropathy and 11% peripheral artery disease (PAD) prevalence was assumed. Primary prevention (PP) was defined as reducing initial DFU incidence. Results: PP was more than 90% likely to provide cost-savings when annual prevention costs are less than $50/person and/or annual DFU incidence is reduced by at least 25%. Efforts directed at patients with diabetes who were at moderate or high risk for DFUs were very likely to provide cost-savings if DFU incidence was decreased by at least 10% and/or the cost was less than $150 per person per year. Conclusions: Low-cost DFU primary prevention efforts producing even small decreases in DFU incidence may provide the best opportunity for cost-savings, especially if focused on patients with neuropathy and/or PAD. Mobile phone-based reminders, self-identification of risk factors (ex. Ipswich touch test), and written brochures may be among such low-cost interventions that should be investigated for cost-savings potential.ArticlePublication Metadata only A fluid approximation for the single-leg fare allocation problem with nonhomogeneous poisson demand(Springer, 2022-02-27) Korkmaz, Mehmet Selçuk; Kundakcıoğlu, Ömer Erhun; Sivrikaya, O.; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Korkmaz, Mehmet SelçukFare allocation for legs and O&D pairs plays a crucial role in airline revenue management. Despite a large number of dynamic pricing studies, there are only a few widely adopted studies in which assumptions affect most tactical decisions with potentially large impacts on airline profitability. These decisions involve approximating future pricing schemes, allocation of fare classes, and setting booking limits. We propose a fare allocation model for a single leg in the presence of a realistic nonhomogeneous Poisson demand with an increasing rate. We aim to compute when and how to markup the price for an airfare product (switch to an upper fare class) to maximize the expected revenue. We study a fluid approximation of the underlying stochastic problem considering independent demand from each customer segment and examine different properties that lead to several important insights. Finally, we propose a dynamic look-ahead pricing scheme to compare our fluid approximation results against the well-known EMSRb heuristic and a dynamic programming solution on randomly generated booking request data. Numerical examples illustrate the effectiveness of our proposed approach.ArticlePublication Metadata only Aid allocation for camp-based and urban refugees with uncertain demand and replenishments(Wiley, 2021-12) Azizi, S.; Bozkır, Cem Deniz Çağlar; Trapp, A. C.; Kundakcıoğlu, Ömer Erhun; Kurbanzade, Ali Kaan; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Bozkır, Cem Deniz Çağlar; Kurbanzade, Ali KaanThere are 26 million refugees worldwide seeking safety from persecution, violence, conflict, and human rights violations. Camp-based refugees are those that seek shelter in refugee camps, whereas urban refugees inhabit nearby, surrounding populations. The systems that supply aid to refugee camps may suffer from ineffective distribution due to challenges in administration, demand uncertainty and volatility in funding. Aid allocation should be carried out in a manner that properly balances the need of ensuring sufficient aid for camp-based refugees, with the ability to share excess inventory, when available, with urban refugees that at times seek nearby camp-based aid. We develop an inventory management policy to govern a camp's sharing of aid with urban refugee populations in the midst of uncertainties related to camp-based and urban demands, and replenishment cycles due to funding issues. We use the policy to construct costs associated with: (i) referring urban populations elsewhere, (ii) depriving camp-based refugee populations, and (iii) holding excess inventory in the refugee camp system. We then seek to allocate aid in a manner that minimizes the expected overall cost to the system. We propose two approaches to solve the resulting optimization problem, and conduct computational experiments on a real-world case study as well as on synthetic data. Our results are complemented by an extensive simulation study that reveals broad support for our optimal thresholds and allocations to generalize across varied key parameters and distributions. We conclude by presenting related discussions that reveal key managerial insights into humanitarian aid allocation under uncertainty.ArticlePublication Metadata only A mathematical model for perishable products with price- and displayed-stock-dependent demand(Elsevier, 2016-12) Onal, M.; Yenipazarli, A.; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer ErhunWe introduce an economic order quantity model that incorporates product assortment, pricing and space-allocation decisions for a group of perishable products. The goal is to maximize the retailer’s profit under shelf-space and backroom storage capacity constraints. We assume that the demand rate of a product is a function of the selling prices and the displayed stock levels of all the products in the assortment. We propose a Tabu Search based heuristic method to solve this complex problem.ArticlePublication Metadata only Cost of fairness in agent scheduling for contact centers(American Institute of Mathematical Sciences, 2022-03) Şimşek, Onur; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Şimşek, OnurWe study a workforce scheduling problem faced in contact centers with considerations on a fair distribution of shifts in compliance with agentpreferences. We develop a mathematical model that aims to minimize operatingcosts associated with labor, transportation of agents, and lost customers.Aside from typical work hour-related constraints, we also try to conform withagents' preferences for shifts, as a measure of fairness. We plot the trade-off between agent satisfaction and total operating costs for Vestel, one of Turkey'slargest consumer electronics companies. We present insights on the increasedcost to have content and a fair environment on several agent availability scenarios.ArticlePublication Metadata only Mathematical optimization for time series decomposition(Springer, 2021-09) Gözüyılmaz, Şeyma; Kundakcıoğlu, Ömer Erhun; Industrial Engineering; KUNDAKCIOĞLU, Ömer Erhun; Gözüyılmaz, ŞeymaDecomposing time series into trend and seasonality components reveals insights used in forecasting and anomaly detection. This study proposes a mathematical optimization approach that addresses several data-related issues in time series decomposition. Our approach does not only handle longer and multiple seasons but also identifies outliers and trend shifts. Numerical experiments on real-world and synthetic problem sets present the effectiveness of the proposed approach.