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Publication Open Access A model-based heuristic to the min max K-arc routing for connectivity problem(Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2014) Akbari, Vahid; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We consider the post-disaster road clearing problem with the goal of restoring network connectivity in shortest time. Given a set of blocked edges in the road network, teams positioned at depot nodes are dispatched to open a subset of them that reconnects the network. After a team finishes working on an edge, others can traverse it. The problem is to find coordinated routes for the teams. We generate a feasible solution using a constructive heuristic algorithm after solving a relaxed mixed integer program. In almost 70 percent of the instances generated both randomly and from Istanbul data, the relaxation solution turned out to be feasible, i.e. optimal for the original problem.Publication Open Access A new identification method of specific cutting coefficients for ball end milling(Elsevier, 2014) Department of Mechanical Engineering; Khavidaki, Sayed Ehsan Layegh; Lazoğlu, İsmail; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391The paper presents a new and accurate strategy for estimation of cutting coefficients for ball-end milling of free form surfaces in 3- and 5-axis operations. Since the cutting coefficients are not constant along the tool axis in the ball part of the cutter, the tool is considered by dividing the ball region into thin disks. In order to find the contribution of each disk to resultant cutting force, an experimental setup is designed to cut the workpiece while only that disk is in engaged with the workpiece. It is shown that this method is more efficient than common methods of mechanistic identification of cutting constants that are available in literature. The derivations are improved by considering the helix angle and cutting edge length to enhance the accuracy of the estimated cutting coefficients. Validation of the proposed strategy is demonstrated experimentally by simulation of cutting forces and comparing the results with conventional methods of identification of cutting coefficients that have been proposed in the literature. (C) 2014 Elsevier B.V.
Publication Metadata only Admission policies for a two class loss system(Taylor and Francis, 2001) Burnetas, Apostolos; Van Der Wal, Jan; Department of Industrial Engineering; Örmeci, Lerzan; Faculty Member; Department of Industrial Engineering; College of Engineering; 32863We consider the problem of dynamic admission control in a Markovian loss queueing system with two classes of customers with different service rates and revenues. We show that under certain conditions, customers of one class, which we call a preferred class, are always admitted to the system. Moreover, the optimal policy is of threshold type, and we establish that the thresholds are monotone under very restrictive conditions. Copyright 2001 by Marcel Dekker, Inc.Publication Metadata only Aggregate planning problem from sustainability perspective(IEEE, 2015) Department of Industrial Engineering; Türkay, Metin; Saraçoğlu, Öztürk; Arslan, Mehmet Can; Faculty Member; PhD Student; PhD Student; Department of Industrial Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/AAggregate planning refers to the determination of production, inventory and capacity levels for a medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria from sustainability perspective. We analyze the revised models and propose results that would be insightful for decision makers. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning.Publication Metadata only An new approach to optimum component testing problem incorporating expected system lifetime(IFAC, 2009) Yamangil, Emre; Kuban Altinel I.; Feyziog̃lu, Orhan; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631We analyze the component testing problem of devices which consist of series connection of redundant, standby redundant and k-out-of-n subsystems. Although system reliability is a common performance measure, here we extend previous studies by considering expected system lifetime. This case applies when setting mission time for a system is more practical than deciding on system reliability accurately. The problem is formulated as a semi-infinite linear programming problem, and the optimum test times are obtained with a column generation technique incorporating reverse convex programming. The proposed solution technique is also illustrated by numerical examples.Publication Metadata only Analysis of mobile phone call data of İstanbul residents(IGI Global, 2015) Department of Industrial Engineering; Department of Industrial Engineering; N/A; Salman, Fatma Sibel; Sivaslıoğlu, Erbil; Memiş, Burak; Faculty Member; Undergraduate Student; PhD Student; Department of Industrial Engineering; College of Engineering; College of Engineering; Graduate School of Business; 178838; N/A; N/AIn this chapter, we analyze call detail records of subscribers of a major cellular network provider in Turkey with a focus on subscribers that reside in Istanbul. We consider a sample of 10,000 opt-in subscribers, chosen proportionally to the population density of each district of Istanbul. The anonymized cell phone usage data for 6 weeks are combined with demographic and subscription package attributes. Our methodology consists of data retrieval and cleaning, analysis and visualization. The analysis aims to extract information to be used mainly in disaster preparedness, marketing and public service design, and is categorized under: 1) understanding call habits in terms of call duration and call location with respect to gender and age categories, 2) tracking population density changes by time and district, 3) segmentation of people visiting specified locations, 4) information on mobility of disabled subscribers, and 5) international travel patterns by roaming data analysis.Publication Metadata only Contributions to stochastic models of manufacturing and service operations(Taylor & Francis, 2018) Liberopoulos, George; Heavey, Cathal; Helber, Stefan; Matta, Andrea; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579N/APublication Metadata only Environmentally conscious supply chain management(Wiley-VCH, 2011) Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956N/APublication Metadata only Hybrid flow-shop: a memetic algorithm using constraint-based scheduling for efficient search(Springer Nature, 2009) Sevaux, Marc; Jouglet, Antoine; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033The paper considers the hybrid flow-shop scheduling problem with multiprocessor tasks. Motivated by the computational complexity of the problem, we propose a memetic algorithm for this problem in the paper. We first describe the implementation details of a genetic algorithm, which is used in the memetic algorithm. We then propose a constraint programming based branch-and-bound algorithm to be employed as the local search engine of the memetic algorithm. Next, we present the new memetic algorithm. We lastly explain the computational experiments carried out to evaluate the performance of three algorithms (genetic algorithm, constraint programming based branch-and-bound algorithm, and memetic algorithm) in terms of both the quality of the solutions produced and the efficiency. These results demonstrate that the memetic algorithm produces better quality solutions and that it is very efficient.Publication Open Access Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation(BioMed Central, 2016) Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find gene sets on which phenotypes are linearly dependent by bringing prior biological knowledge into the analysis, which may not capture more complex nonlinear dependencies. Thus, formulating an integrated model of gene set analysis and nonlinear predictive modeling is of great practical importance. In this study, we propose a Bayesian binary classification framework to integrate gene set analysis and nonlinear predictive modeling. We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly. Our main novelty is the probabilistic nonlinear formulation that enables us to robustly capture nonlinear dependencies between genomic data and phenotype even with small sample sizes. We demonstrate the performance of our algorithms using repeated random subsampling validation experiments on two cancer and two tuberculosis datasets by predicting important disease phenotypes from genome-wide gene expression data. We are able to obtain comparable or even better predictive performance than a baseline Bayesian nonlinear algorithm and to identify sparse sets of relevant genes and gene sets on all datasets. We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes.