Organizational Unit: Department of Industrial Engineering
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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 Parallel computing in Asian option pricing(Elsevier Science Bv, 2007) Sak, Halis; Boduroglu, Ilkay; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631We discuss the use of parallel computing in Asian option pricing and evaluate the efficiency of various algorithms. We only focus on "backward-starting fixed strike" Asian options that are continuously averaged. We implement a partial differential equation (PDE) approach that involves a single state variable to price the Asian option, and implement the same methodology to price a standard European option to check for accuracy. A parabolic PDE is solved by using both explicit and Crank-Nicolson's implicit finite-difference methods. In particular, we look for algorithms designed for implementing the computations in massively parallel processors (MPP). We evaluate the performance of the algorithms by comparing the numerical results with respect to accuracy and wall-clock time of code executions. Codes are executed on a Linux PC cluster.Publication Metadata only Cytokine response in crimean-congo hemorrhagic fever virus infection(Wiley, 2017) Eren, Şebnem; Çelikbaş, Aysel; Baykam, Nurcan; Dokuzoğuz, Başak; N/A; N/A; Department of Industrial Engineering; N/A; Ergönül, Önder; Şeref, Ceren; Gönen, Mehmet; Can, Füsun; Faculty Member; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; Graduate School of Health Sciences; College of Engineering; School of Medicine; Koç University Hospital; 110398; N/A; 237468; 103165We described the predictive role of cytokines in fatality of Crimean Congo Hemorrhagic Fever Virus (CCHFV) infection by using daily clinical sera samples. Consequent serum samples of the selected patients in different severity groups and healthy controls were examined by using human cytokine 17-plex assay. We included 12 (23%) mild, 30 (58%) moderate, 10 (19%) severe patients, and 10 healthy volunteers. The mean age of the patients was 52 (sd 15), 52% were female. Forty-six patients (88%) received ribavirin. During disease course, the median levels of IL-6, IL-8, IL-10, IL-10/12, IFN-gamma, MCP-1, and MIP-1b were found to be significantly higher among CCHF patients than the healthy controls. Within the first 5 days after onset of disease, among the fatal cases, the median levels of IL-6 and IL-8 were found to be significantly higher than the survived ones (Fig. 3), and MCP-1 was elevated among fatal cases, but statistical significance was not detected. In receiver operating characteristic (ROC) analysis, IL-8 (92%), IL-6 (92%), MCP-1 (79%) were found to be the most significant cytokines in predicting the fatality rates in the early period of the disease (5 days). IL-6 and IL-8 can predict the poor outcome, within the first 5 days of disease course. Elevated IL-6 and IL-8 levels within first 5 days could be used as prognostic markers.Publication Metadata only An adaptive and diversified vehicle routing approach to reducing the security risk of cash-in-transit operations(Wiley, 2017) Bozkaya, Burçin; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Telciler, Kaan; Faculty Member; Master Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 178838; N/AWe consider the route optimization problem of transporting valuables in cash-in-transit (CIT) operations. The problem arises as a rich variant of the capacitated vehicle routing problem (CVRP) with time windows and pickup and deliveries. Due to the high-risk nature of this operation (e.g., robberies) we consider a bi-objective function where we attempt to minimize the total transportation cost and the security risk of transporting valuables along the designed routes. For risk minimization, we propose a composite risk measure that is a weighted sum of two risk components: (i) following the same or very similar routes, and (ii) visiting neighborhoods with low socioeconomic status along the routes. We also consider vehicle capacities in terms of monetary value carried as per insurance regulations. We develop an adaptive randomized bi-objective path selection algorithm that uses the composite risk measure in choosing alternative paths between origin-destination pairs over a sequence of days. We solve the rich CVRP approximately for each day with updated costs. We test our solution approach on a data set from a CIT delivery service provider and provide insights on how the routes diversify daily. Our approach generates a spectrum of solutions with costrisk trade-off to support decision making.Publication Metadata only Multi-vehicle synchronized arc routing problem to restore post-disaster network connectivity(Elsevier Science Bv, 2017) Department of Industrial Engineering; Department of Industrial Engineering; Akbari, Vahid; Salman, Fatma Sibel; Teaching Faculty; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; N/A; 178838After a natural disaster roads can be damaged or blocked by debris, while bridges and viaducts may collapse. This commonly observed hazard causes some road sections to be closed and may even disconnect the road network. In the immediate disaster response phase work teams are dispatched to open a subset of roads to reconnect the network. Closed roads are traversable only after they are unblocked/cleared by one of the teams. The main objective of this research is to provide an efficient solution method to generate a synchronized work schedule for the road clearing teams. The solution should specify the synchronized routes of each clearing team so that: 1) connectivity of the network is regained, and 2) none of the closed roads are traversed unless their unblocking/clearing procedure is finished. In this study we develop an exact Mixed Integer Programming (MIP) formulation to solve this problem. Furthermore, we propose a matheuristic that is based on an MIP-relaxation and a local search algorithm. We prove that the optimality gap of the relaxation solution is bounded by K times the lower bound obtained from the relaxed model, where K is the number of teams. We show computationally that the matheuristic obtains optimal or near-optimal solutions. (C) 2016 Elsevier B.V. All rights reserved.Publication Metadata only Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors(Amer Chemical Soc, 2009) N/A; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Dağlıyan, Onur; Kavaklı, İbrahim Halil; Türkay, Metin; Master Student; Faculty Member; Faculty Member; Department of Chemical and Biological Engineering; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 40319; 24956Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure based drug discovery. However, the relationship between binding free energies and biological activities (pIC(50)) of drug candidates is sfill an unsolved issue that limits the efficiency and speed of drug development processes. In this study, the relationship between them is investigated based on a common molecular descriptor set for human cytochrome P450 enzymes (CYPs). CYPs play an important role in drug-drug interactions, drug metabolism, and toxicity. Therefore, in silico prediction of CYP inhibition by drug candidates is one of the major considerations in drug discovery. The combination of partial leastsquares regression (PLSR) and a variety of classification algorithms were employed by considering this relationship as a classification problem. Our results indicate that PLSR with classification is a powerful tool to predict more than one output such as binding free energy and pIC(50) simultaneously. PLSR with mixedinteger linear programming based hyperboxes predicts the binding free energy and pIC(50) with a mean accuracy of 87.18% (min: 81.67% max: 97.05%) and 88.09% (min: 79.83% max: 92.90%), respectively, for the cytochrome p450 superfamily using the common 6 molecular descriptors with a 10-fold cross- val idati on.Publication Metadata only Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies(Oxford Univ Press, 2017) Nikolova, Olga; Moser, Russell; Kemp, Christopher; Margolin, Adam A.; Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Motivation: In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments. Results: We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.Publication Metadata only Managing home health-care services with dynamic arrivals during a public health emergency(Institute of Electrical and Electronics Engineers (IEEE), 2022) Araz, Ozgur M.; N/A; Department of Industrial Engineering; N/A; Çınar, Ahmet; Salman, Fatma Sibel; Parçaoğlu, Mert; PhD Student; Faculty Member; Master Student; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; Graduate School of Sciences and Engineering; N/A; 178838; N/AWe consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches.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 Bounded rationality in clearing service systems(Elsevier, 2020) Department of Industrial Engineering; Canbolat, Pelin Gülşah; Faculty Member; Department of Industrial Engineering; College of Engineering; 108242This paper considers a clearing service system where customers arrive according to a Poisson process, and decide to join the system or to balk in a boundedly rational manner. It assumes that all customers in the system are served at once when the server is available and times between consecutive services are independently and identically distributed random variables. Using logistic quantal-response functions to model bounded rationality, it first characterizes customer utility and system revenue for fixed price and degree of rationality, then solves the pricing problem of a revenue-maximizing system administrator. The analysis of the resulting expressions as functions of the degree of rationality yields several insights including: (i) for an individual customer, it is best to be perfectly rational if the price is fixed; however, when customers have the same degree of rationality and the administrator prices the service accordingly, a finite nonzero degree of rationality uniquely maximizes customer utility, (ii) system revenue grows arbitrarily large as customers tend to being irrational, (iii) social welfare is maximized when customers are perfectly rational, (iv) in all cases, at least 78% of social welfare goes to the administrator. The paper also considers a model where customers are heterogeneous with respect to their degree of rationality, explores the effect of changes in distributional parameters of the degree of rationality for fixed service price, provides a characterization for the revenue-maximizing price, and discusses the analytical difficulties arising from heterogeneity in the degree of bounded rationality. (C) 2019 Elsevier B.V. All rights reserved.