Publications with Fulltext
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access Modelling and analysis of the impact of correlated inter-event data on production control using Markovian arrival processes(Springer, 2019) Department of Business Administration; Department of Industrial Engineering; N/A; Tan, Barış; Dizbin, Nima Manafzadeh; Faculty Member; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; Graduate School of Business; 28600; N/AEmpirical studies show that the inter-event times of a production system are correlated. However, most of the analytical studies for the analysis and control of production systems ignore correlation. In this study, we show that real-time data collected from a manufacturing system can be used to build a Markovian arrival processes (MAP) model that captures correlation in inter-event times. The obtained MAP model can then be used to control production in an effective way. We first present a comprehensive review on MAP modeling and MAP fitting methods applicable to manufacturing systems. Then we present results on the effectiveness of these fitting methods and discuss how the collected inter-event data can be used to represent the flow dynamics of a production system accurately. In order to study the impact of capturing the flow dynamics accurately on the performance of a production control system, we analyze a manufacturing system that is controlled by using a base-stock policy. We study the impact of correlation in inter-event times on the optimal base-stock level of the system numerically by employing the structural properties of the MAP. We show that ignoring correlated arrival or service process can lead to overestimation of the optimal base-stock level for negatively correlated processes, and underestimation for the positively correlated processes. We conclude that MAPs can be used to develop data-driven models and control manufacturing systems more effectively by using shop-floor inter-event data.Publication Open Access On the complexity and approximation of the maximum expected value all-or-nothing subset(2018) Goldberg, Noam; Department of Industrial Engineering; Rudolf, Gabor; Faculty Member; Department of Industrial Engineering; College of EngineeringAn unconstrained nonlinear binary optimization problem of selecting a maximum expected value subset of items is considered. Each item is associated with a profit and probability. Each of the items succeeds or fails independently with the given probabilities, and the profit is obtained in the event that all selected items succeed. The objective is to select a subset that maximizes the total value times the product of probabilities of the chosen items. The problem is proven NP-hard by a nontrivial reduction from subset sum. Then we develop a fully polynomial time approximation scheme (FPTAS) for this problem.Publication Open Access Structural properties of a class of robust inventory and queueing control problems(Wiley, 2018) Department of Industrial Engineering; N/A; Örmeci, Lerzan; Karaesmen, Fikri; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; 32863; 3579; N/AIn standard stochastic dynamic programming, the transition probability distributions of the underlying Markov Chains are assumed to be known with certainty. We focus on the case where the transition probabilities or other input data are uncertain. Robust dynamic programming addresses this problem by defining a min-max game between Nature and the controller. Considering examples from inventory and queueing control, we examine the structure of the optimal policy in such robust dynamic programs when event probabilities are uncertain. We identify the cases where certain monotonicity results still hold and the form of the optimal policy is determined by a threshold. We also investigate the marginal value of time and the case of uncertain rewards.Publication Open Access The role of AcrAB-TolC Efflux pumps on quinolone resistance of E-coli ST131(Springer, 2018) Kurt-Azap, O.; Dolapcı, I.; Yeşilkaya, A.; Can, F.; N/A; Department of Industrial Engineering; Ergönül, Önder; Ataç, Nazlı; Gönen, Mehmet; Faculty Member; Researcher; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 110398; N/A; 237468Escherichia coli ST131 is a cause for global concern because of its high multidrug resistance and several virulence factors. In this study, the contribution of acrAB-TolC efflux system of E. coli ST131 to fluoroquinolone resistance was evaluated. A total of nonrepetitive 111 ciprofloxacin-resistant E. coli isolates were included in the study. Multilocus sequence typing was used for genotyping. Expressions of acrA, acrB, and TolC efflux pump genes were measured by RT-PCR. Mutations in marA, gyrA, parC, and aac(6)-lb-cr positivity were studied by Sanger sequencing. Sixty-four (57.7%) of the isolates were classified as ST131, and 52 (81.3%) of the ST131 isolates belonged to H30-Rx subclone. In ST131, CTX-M 15 positivity (73%) and aac(6)-lb-cr carriage (75%) were significantly higher than those in non-ST131 (12.8% and 51%, respectively) (P<0.05). The ampicillin-sulbactam (83%) resistance was higher, and gentamicin resistance (20%) was lower in ST131 than that in non-ST131 (64% and 55%, respectively) (P=0.001 and P=0.0002). Numbers of the isolates with MDR or XDR profiles did not differ in both groups. Multiple in-dels (up to 16) were recorded in all quinolone-resistant isolates. However, marA gene was more overexpressed in ST131 compared to that in non-ST131 (median 5.98 vs. 3.99; P=0.0007). Belonging to H30-Rx subclone, isolation site, ciprofloxacin MIC values did not correlate with efflux pump expressions. In conclusion, the marA regulatory gene of AcrAB-TolC efflux pump system has a significant impact on quinolone resistance and progression to MDR profile in ST131 clone. Efflux pump inhibitors might be alternative drugs for the treatment of infections caused by E. coli ST131 if used synergistically in combination with antibiotics.Publication Open Access Distributionally robust optimization under a decision-dependent ambiguity set with applications to machine scheduling and humanitarian logistics(The Institute for Operations Research and the Management Sciences (INFORMS), 2022) Noyan, Nilay; Lejeune, Miguel; Department of Industrial Engineering; Rudolf, Gabor; Faculty Member; Department of Industrial Engineering; College of EngineeringWe introduce a new class of distributionally robust optimization problems under decision-dependent ambiguity sets. In particular, as our ambiguity sets, we consider balls centered on a decision-dependent probability distribution. The balls are based on a class of earth mover's distances that includes both the total variation distance and the Wasserstein metrics. We discuss the main computational challenges in solving the problems of interest and provide an overview of various settings leading to tractable formulations. Some of the arising side results, such as the mathematical programming expressions for robustified risk measures in a discrete space, are also of independent interest. Finally, we rely on state-of-the-art modeling techniques from machine scheduling and humanitarian logistics to arrive at potentially practical applications, and present a numerical study for a novel risk-averse scheduling problem with controllable processing times. Summary of Contribution: In this study, we introduce a new class of optimization problems that simultaneously address distributional and decision-dependent uncertainty. We present a unified modeling framework along with a discussion on possible ways to specify the key model components, and discuss the main computational challenges in solving the complex problems of interest. Special care has been devoted to identifying the settings and problem classes where these challenges can be mitigated. In particular, we provide model reformulation results, including mathematical programming expressions for robustified risk measures, and describe how these results can be utilized to obtain tractable formulations for specific applied problems from the fields of humanitarian logistics and machine scheduling. Toward demonstrating the value of the modeling approach and investigating the performance of the proposed mixed-integer linear programming formulations, we conduct a computational study on a novel risk-averse machine scheduling problem with controllable processing times. We derive insights regarding the decision-making impact of our modeling approach and key parameter choices.Publication Open Access Analysis of a group purchasing organization under demand and price uncertainty(Springer, 2018) Department of Business Administration; Department of Industrial Engineering; Tan, Barış; Karabağ, Oktay; Faculty Member; Resercher; Department of Business Administration; Department of Industrial Engineering; College of Administrative Sciences and Economics; College of Engineering; 28600; N/ABased on an industrial case study, we present a stochastic model of a supply chain consisting of a set of buyers and suppliers and a group purchasing organization (GPO). The GPO combines orders from buyers in a two-period model. Demand and price in the second period are random. An advance selling opportunity is available to all suppliers and buyers in the first-period market. Buyers decide how much to buy through the GPO in the first period and how much to procure from the market at a lower or higher price in the second period. Suppliers determine the amount of capacity to sell through the GPO in the first period and to hold in reserve in order to meet demand in the second period. The GPO conducts a uniform-price reverse auction to select suppliers and decides on the price that will be offered to buyers to maximize its profit. By determining the optimal decisions of buyers, suppliers, and the GPO, we answer the following questions: Do suppliers and buyers benefit from working with a GPO? How do the uncertainty in demand, the share of GPO orders in the advance sales market, and the uncertainty in price influence the players' decisions and profits? What are the characteristics of an environment that would encourage suppliers and buyers to work with a GPO? We show that a GPO helps buyers and suppliers to mitigate demand and price risks effectively while collecting a premium by serving as an intermediary between them.Publication Open Access Classification of drug molecules considering their IC(50) values using mixed-integer linear programming based hyper-boxes method(BioMed Central, 2008) Department of Industrial Engineering; Department of Chemical and Biological Engineering; Armutlu, Pelin; Özdemir, Muhittin Emre; Yüksektepe, Fadime Üney; Kavaklı, İbrahim Halil; Türkay, Metin; Faculty Member; Department of Industrial Engineering; Department of Chemical and Biological Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; N/A; N/A; N/A; 40319; 24956Background: A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC(50) values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results: We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC(50) values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naive Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion: Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed.Publication Open Access Inventory policies for two products under Poisson demand: interaction between demand substitution, limited storage capacity and replenishment time uncertainty(Wiley, 2018) Burnetas, Apostolos; Department of Industrial Engineering; Kanavetas, Odysseas; Faculty Member; Department of Industrial Engineering; College of EngineeringWe consider a two-product inventory system with independent Poisson demands, limited joint storage capacity and partial demand substitution. Replenishment is performed simultaneously for both products and the replenishment time may be fixed or exponentially distributed. For both cases we develop a Continuous Time Markov Chain model for the inventory levels and derive expressions for the expected profit per unit time. We establish analytic expressions for the profit function and show that it satisfies decreasing differences properties in the order quantities, which allows for a more efficient algorithm to determine the optimal ordering policy. Using computational experiments, we assess the effect of substitution and replenishment time uncertainty on the order quantities and the profit as a function of the storage capacity.Publication Open Access PrognosiT: pathway/gene set-based tumour volume prediction using multiple kernel learning(BioMed Central, 2021) Department of Industrial Engineering; N/A; Gönen, Mehmet; Bektaş, Ayyüce Begüm; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; Graduate School of Sciences and Engineering; 237468; N/ABackground: identification of molecular mechanisms that determine tumour progression in cancer patients is a prerequisite for developing new disease treatment guidelines. Even though the predictive performance of current machine learning models is promising, extracting significant and meaningful knowledge from the data simultaneously during the learning process is a difficult task considering the high-dimensional and highly correlated nature of genomic datasets. Thus, there is a need for models that not only predict tumour volume from gene expression data of patients but also use prior information coming from pathway/gene sets during the learning process, to distinguish molecular mechanisms which play crucial role in tumour progression and therefore, disease prognosis. Results: in this study, instead of initially choosing several pathways/gene sets from an available set and training a model on this previously chosen subset of genomic features, we built a novel machine learning algorithm, PrognosiT, that accomplishes both tasks together. We tested our algorithm on thyroid carcinoma patients using gene expression profiles and cancer-specific pathways/gene sets. Predictive performance of our novel multiple kernel learning algorithm (PrognosiT) was comparable or even better than random forest (RF) and support vector regression (SVR). It is also notable that, to predict tumour volume, PrognosiT used gene expression features less than one-tenth of what RF and SVR algorithms used. Conclusions: PrognosiT was able to obtain comparable or even better predictive performance than SVR and RF. Moreover, we demonstrated that during the learning process, our algorithm managed to extract relevant and meaningful pathway/gene sets information related to the studied cancer type, which provides insights about its progression and aggressiveness. We also compared gene expressions of the selected genes by our algorithm in tumour and normal tissues, and we then discussed up- and down-regulated genes selected by our algorithm while learning, which could be beneficial for determining new biomarkers.