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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3
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Publication Metadata only Portfolio optimization in stochastic markets(Springer Heidelberg, 2006) Cakmak, U; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631We consider a multiperiod mean-variance model where the model parameters change according to a stochastic market. The mean vector and covariance matrix of the random returns of risky assets all depend on the state of the market during any period where the market process is assumed to follow a Markov chain. Dynamic programming is used to solve an auxiliary problem which, in turn, gives the efficient frontier of the mean-variance formulation. An explicit expression is obtained for the efficient frontier and an illustrative example is given to demonstrate the application of the procedure.Publication Metadata only Optimal threshold levels in stochastic fluid models via simulation-based optimization(Springer, 2007) Gurkan, Gul; Ozdemir, Ozge; Department of Industrial Engineering; Karaesmen, Fikri; Faculty Member; Department of Industrial Engineering; College of Engineering; 3579A number of important problems in production and inventory control involve optimization of multiple threshold levels or hedging points. We address the problem of finding such levels in a stochastic system whose dynamics can be modelled using generalized semi-Markov processes (GSMP). The GSMP framework enables us to compute several performance measures and their sensitivities from a single simulation run for a general system with several states and fairly general state transitions. We then use a simulation-based optimization method, sample-path optimization, for finding optimal hedging points. We report numerical results for systems with more than twenty hedging points and service-level type probabilistic constraints. In these numerical studies, our method performed quite well on problems which are considered very difficult by current standards. Some applications falling into this framework include designing manufacturing flow controllers, using capacity options and subcontracting strategies, and coordinating production and marketing activities under demand uncertainty.Publication Metadata only Prediction of folding type of proteins using mixed-integer linear programming(Elsevier Science Bv, 2005) Department of Industrial Engineering; Department of Industrial Engineering; N/A; Türkay, Metin; Yüksektepe, Fadime Üney; Yılmaz, Özlem; Faculty Member; Researcher; Master Student; Department of Industrial Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/AProteins are classified into four main structural classes by considering their amino acid compositions. Traditional approaches that use hyperplanes to partition data sets into two groups perform poorly due to the existence of four classes. Therefore, a novel method that uses mixed-integer programming is developed to overcome difficulties and inconsistencies of these traditional approaches. Mixed-integer programming (MIP) allows the use of hyper-boxes in order to define the boundaries of the sets that include all or some of the points in that class. For this reason, the efficiency and accuracy of data classification with MIP approach can be improved dramatically compared to the traditional methods. The efficiency of the proposed approach is illustrated on a training set of 120 proteins (30 from each type). The prediction results and their validation are also examined.Publication Metadata only Semi-Markov modulated Poisson process: probabilistic and statistical analysis(Springer, 2006) Soyer, R.; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631We consider a Poisson process that is modulated in such a way that the arrival rate at any time depends on the state of a semi-Markov process. This presents an interesting generalization of Poisson processes with important implications in real life applications. Our analysis concentrates on the transient as well as the long term behaviour of the arrival count and the arrival time processes. We discuss probabilistic as well as statistical issues related to various quantities of interest.Publication Metadata only Multiple facility location on a network with linear reliability order of edges(Springer, Van Godewijckstraat, 2017) Hassin, Refael; Ravi, R.; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We study the problem of locating facilities on the nodes of a network to maximize the expected demand serviced. The edges of the input graph are subject to random failure due to a disruptive event. We consider a special type of failure correlation. The edge dependency model assumes that the failure of a more reliable edge implies the failure of all less reliable ones. Under this dependency model called Linear Reliability Order (LRO) we give two polynomial time exact algorithms. When two distinct LRO's exist, we prove the total unimodularity of a linear programming formulation. In addition, we show that minimizing the sum of facility opening costs and expected cost of unserviced demand under two orderings reduces to a matching problem. We prove NP-hardness of the three orderings case and show that the problem with an arbitrary number of orderings generalizes the deterministic maximum coverage problem. When a demand point can be covered only if a facility exists within a distance limit, we show that the problem is NP-hard even for a single ordering.Publication Metadata only Modeling earthquake vulnerability of highway networks(Elsevier, 2013) N/A; Department of Industrial Engineering; Arşık, İdil; Salman, Fatma Sibel; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 178838In this study, we investigate the earthquake vulnerability of highway networks whose links are subject to failure. We propose a model called α- conservative failure model that aims to capture the dependency among link failures in the event of an earthquake. According to this model, we calculate a path-based accessibility measure to assess the expected weighted average shortest distance to serve a unit demand after the earthquake. We test the proposed link failure model on a case study of the earthquake vulnerability in Istanbul.Publication Metadata only The parallel genetic algorithm for designing dna randomizations in a combinatorial protein experiment(Springer-Verlag Berlin, 2006) Blazewicz, Jacek; Dziurdza, Beniamin; Markiewicz, Wojciech T; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033Evolutionary methods of protein engineering such as phage display have revolutionized drug design and the means of studying molecular binding. In order to obtain the highest experimental efficiency, the distributions of constructed combinatorial libraries should be carefully adjusted. The presented approach takes into account diversity- completeness trade-off and tries to maximize the number of new amino acid sequences generated in each cycle of the experiment. In the paper, the mathematical model is introduced and the parallel genetic algorithm for the defined optimization problem is described. Its implementation on the SunFire 6800 computer proves a high efficiency of the proposed approach.Publication Metadata only The approximability of multiple facility location on directed networks with random arc failures(Springer, 2020) Hassin, Refael; Ravi, R.; Segev, Danny; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We introduce and study the maximum reliability coverage problem, where multiple facilities are to be located on a network whose arcs are subject to random failures. Our model assumes that arcs fail independently with non-uniform probabilities, and the objective is to locate a given number of facilities, aiming to maximize the expected demand serviced. In this context, each demand point is said to be serviced (or covered) when it is reachable from at least one facility by an operational path. The main contribution of this paper is to establish tight bounds on the approximability of maximum reliability coverage on bidirected trees as well as on general networks. Quite surprisingly, we show that this problem is NP-hard on bidirected trees via a carefully-constructed reduction from the partition problem. On the positive side, we make use of approximate dynamic programming ideas to devise an FPTAS on bidirected trees. For general networks, while maximum reliability coverage is (1-1/e+epsilon)-inapproximable as an extension of the maxk-cover problem, even estimating its objective value is #P-complete, due to generalizing certain network reliability problems. Nevertheless, we prove that by plugging-in a sampling-based additive estimator into the standard greedy algorithm, a matching approximation ratio of 1-1/e-epsilon can be attained.Publication Metadata only Scheduling chains with identical jobs and constant delays on a single machine(Springer, 2006) Brucker, P; Knust, S; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033In this paper we study the single-machine problem 1|chains(l), p j = p|∑ C j in which jobs with constant processing times and generalized precedence constraints in form of chains with constant delays are given. One has to schedule the jobs on a single machine such that all delays between consecutive jobs in a chain are satisfied and the sum of all completion times of the jobs is minimized. We show that this problem is polynomially solvable.Publication Metadata only Portfolio selection with imperfect information: a hidden Markov model(Wiley-Blackwell, 2011) N/A; Department of Industrial Engineering; Çanakoğlu, Ethem; Özekici, Süleyman; Researcher; Faculty Member; Department of Industrial Engineering; Graduate School of Social Sciences and Humanities; College of Engineering; 114906; 32631We consider a utility-based portfolio selection problem, where the parameters change according to a Markovian market that cannot be observed perfectly. The market consists of a riskless and many risky assets whose returns depend on the state of the unobserved market process. The states of the market describe the prevailing economic, financial, social, political or other conditions that affect the deterministic and probabilistic parameters of the model. However, investment decisions are based on the information obtained by the investors. This constitutes our observation process. Therefore, there is a Markovian market process whose states are unobserved, and a separate observation process whose states are observed by the investors who use this information to determine their portfolios. There is, of course, a probabilistic relation between the two processes. The market process is a hidden Markov chain and we use sufficient statistics to represent the state of our financial system. The problem is solved using the dynamic programming approach to obtain an explicit characterization of the optimal policy and the value function. In particular, the return-risk frontiers of the terminal wealth are shown to have linear forms. Copyright (C) 2011 John Wiley & Sons, Ltd.
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