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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only A kernel-based multilayer perceptron framework to identify pathways related to cancer stages(Springer International Publishing Ag, 2023) Mokhtaridoost, Milad; Department of Industrial Engineering; Soleimanpoor, Marzieh; Gönen, Mehmet; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of EngineeringStandard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of pathways plays a crucial role in the growth and progression of cancer from early-stage to latestage. That is why we implemented a kernel-based neural network framework that integrates pathways and gene expression data using multiple kernels and discriminates early- and late-stages of cancers. Our goal is to identify the relevant molecular mechanisms of the biological processes which might be driving cancer progression. As the input of developed multilayer perceptron (MLP), we constructed kernel matrices on multiple views of expression profiles of primary tumors extracted from pathways. We used Hallmark and Pathway Interaction Database (PID) datasets to restrict the search area to interpretable solutions. We applied our algorithm to 12 cancer cohorts from the Cancer Genome Atlas (TCGA), including more than 5100 primary tumors. The results showed that our algorithm could extract meaningful and disease-specific mechanisms of cancers. We tested the predictive performance of our MLP algorithm and compared it against three existing classification algorithms, namely, random forests, support vector machines, and multiple kernel learning. Our MLP method obtained better or comparable predictive performance against these algorithms.Publication Metadata only Incorporating patients’ appointment date preferences into decision-making: a simulation and optimization study(Elsevier Ltd, 2024) Ünsal, Özgür; Üster, Halit; Department of Industrial Engineering; Oğuz, Ceyda; Department of Industrial Engineering; College of EngineeringA recent trend in health care is to give patients more flexibility by taking their preferences into account. While this patient-centered approach adds further complexity to the management of operations, it also generates new opportunities for potential improvements in the system. In this study, we show that such an improvement can be obtained via appointment scheduling (AS) systems which are the critical component of any health care delivery system as they can easily be a source of dissatisfaction for the patients as well as for the providers. Accordingly, we propose a novel patient-oriented AS strategy that utilizes patients’ appointment date preferences. The main idea of the strategy is to accumulate patients’ preferences for some amount of time before deciding on their appointments via mathematical optimization, rather than traditional first-call first-booked strategy in which patients are appointed at the time they call. By this way, we aim to exploit the advantage of giving patients preferences to improve the system performance. To examine the proposed AS system with different model settings and problem parameters, we perform a comprehensive simulation study that incorporates several realistic operational features as well as an optimization model for patient to time-slot assignments. Computational results show that using this system can improve not only clinic utility but also patients’ AS experience significantly since it allows more patients to be appointed to one of their convenient dates. This simulation study presents a proof-of-concept for the proposed strategy while providing valuable managerial insights for implementing and operating such an AS system.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 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 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 Route balancing vehicle routing problem with time windows for urban logistics(IEEE, 2019) N/A; Department of Industrial Engineering; Ulusoy, Banu; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956The vehicle routing problem (VRP) has been widely studied in operations research literature with many extensions. This paper studies VRP with time windows and route balance. The objective is to minimize the total number of routes, total cost, total distance, and total time while providing a balance between the routes. We develop a mathematical model to solve small instances of problems. For large instances of problems, we develop a heuristics algorithm. We validate the heuristic algorithm on Solomon benchmark problems. The heuristic algorithm decreases the total number of routes in the solutions by 14%, and total distance of the routes by 12%. We show that the algorithm gives successful results and can be applicable in various areas of logistics.Publication Metadata only A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions(Elsevier, 2017) N/A; N/A; Department of Industrial Engineering; Kabatepe, Bora; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Electric vehicles (EV) are emerging as a mobility solution to reduce emissions in the transportation sector. The studies environmental impact analysis of EVs in the literature are based on the average energy mix or pre-defined generation scenarios and construct policy recommendations with a cost minimization objective. However, the environmental performance of EVs depends on the source of the marginal electricity provided to the grid and single objective models do not provide a thorough analysis on the economic and environmental impacts of EVs. In this paper, these gaps are addressed by a four step methodology that analyzes the effects of EVs under different charging and market penetration scenarios. The methodology includes a bi-criteria optimization model representing the electricity market operations. The results from a real-life case analysis show that EVs decrease costs and emissions significantly compared to conventional vehicles.Publication Metadata only A multi-objective optimization approach for sustainable supply chains incorporating business strategy(IEEE, 2019) N/A; Department of Industrial Engineering; Bozgeyik, Esma Nur; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Sustainability is a necessity in the design and operation of supply chains. the triple bottom line (TBL) accounting of sustainability needs to incorporate economic, environmental and social pillars simultaneously in the decision making process. the business strategy can be developed to promote sustained growth, Also incorporating in the supply chain management issues as a business strategy rather than philanthropy. Deciding on the location of business facilities, supplier-manufacturer network, manufacturer-demand location network and the supplier- manufacturer relation strategy are among the important decisions in business strategy and supply chain management. However, there is a lack of theoretical work which analyzes the business strategy together with TBL concept of sustainability for the supply chain network design problem. in this paper, A methodological approach based on mathematical programming is proposed that conforms to the TBL accounting for supply chain network design problem from suppliers to customers embedded with business strategy and green energy usage option. a realistic case study is applied to the model. the results show that working with inclusive suppliers and using green energy are preferred with highest profit value.Publication Metadata only Discriminating early- and late-stage cancers using multiple kernel learning on gene sets(Oxford Univ Press, 2018) N/A; N/A; Department of Industrial Engineering; Rahimi, Arezou; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Motivation: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early-and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets. Results: In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.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.