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Sayın, Serpil

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Serpil

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Now showing 1 - 10 of 31
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    Preface of the special issue on global multiobjective optimization
    (Springer, 2021) Miettinen, Kaisa; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
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    Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming
    (Springer, 2000) N/A; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    One way of solving multiple objective mathematical programming problems is ending discrete representations of the efficient set. A modified goal of finding good discrete representations of thr efficient set would contribute to the practicality of vector maximization algorithms. We define coverage, uniformity and cardinality as the three attributes of quality of discrete representations and introduce a framework that includes these attributes in which discrete representations can be evaluated, compared to each other, and judged satisfactory or unsatisfactory by a Decision Maker. We provide simple mathematical programming formulation that can he used to compute the coverage error of a given discrete representation. Our formulations are practically implementable when the problem under study is a multiobjective linear programming problem. We believe that the interactive algorithms along with the vector maximization methods can make use of our framework and its tools.
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    Using support vector machines to learn the efficient set in multiple objective discrete optimization
    (Elsevier, 2009) Aytuğ, Haldun; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    We propose using support vector machines (SVMs) to learn the efficient set in multiple objective discrete optimization (MODO). We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As one way of testing this idea, we embed the SVM-approximated efficient set information into a Genetic Algorithm (GA). This is accomplished by using a SVM-based fitness function that guides the GA search. We implement our SVM-guided GA on the multiple objective knapsack and assignment problems. We observe that using SVM improves the performance of the GA compared to a benchmark distance based fitness function and may provide competitive results.
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    A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems
    (Elsevier Science Bv, 2014) N/A; N/A; Department of Business Administration; Kirlik, Gökhan; Sayın, Serpil; PhD Student; Faculty Member; Department of Business Administration; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 6755
    Most real-life decision-making activities require more than one objective to be considered. Therefore, several studies have been presented in the literature that use multiple objectives in decision models. In a mathematical programming context, the majority of these studies deal with two objective functions known as bicriteria optimization, while few of them consider more than two objective functions. In this study, a new algorithm is proposed to generate all nondominated solutions for multiobjective discrete optimization problems with any number of objective functions. In this algorithm, the search is managed over (p - 1)-dimensional rectangles where p represents the number of objectives in the problem and for each rectangle two-stage optimization problems are solved. The algorithm is motivated by the well-known epsilon-constraint scalarization and its contribution lies in the way rectangles are defined and tracked. The algorithm is compared with former studies on multiobjective knapsack and multiobjective assignment problem instances. The method is highly competitive in terms of solution time and the number of optimization models solved.
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    A mixed integer programming formulation for the l-maximin problem
    (Stockton Press, 2000) N/A; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    In this paper, I present a mixed integer programming (MIP) formulation for the 1-maximin problem with rectilinear distance. The problem mainly appears in facility location while trying to locate an undesirable facility. The rectilinear distance is quite Commonly used in the location literature. Our numerical experiments show that one can solve reasonably large location problems using a standard MIP solver. We also provide a linear programming formulation that helps find an upper bound on the objective function value of the 1-maximin problem with any norm when extreme points of the feasible region are known. We discuss various extension alternatives for the MIP formulation.
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    A bicriteria approach to the two-machine flow shop scheduling problem
    (Elsevier Science Bv, 1999) N/A; Department of Business Administration; Department of Business Administration; Sayın, Serpil; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; 6755; 38819
    In this paper we address the problem of minimizing makespan and sum of completion times simultaneously in a two-machine flow shop environment. We formulate the problem as a bicriteria scheduling problem, and develop a branch-and-bound procedure that iteratively solves restricted single objective scheduling problems until the set of efficient solutions is completely enumerated. We report computational results, and explore certain properties of the set of efficient solutions. We then discuss their implications for the Decision Maker.
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    A multiobjective solution method for radiation treatment planning
    (Springer, 2018) Kirlik, Gokhan; Sayin, Serpil; Zhang, Hao Howard; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    The challenge in radiation treatment planning (RTP) is to ensure delivery of a prescription dose to the tumor while limiting the normal tissue toxicity. One way of dealing with this trade off is to use multiobjective optimization which no longer possesses a unique optimal objective function value. In multiobjective optimization, efficient solutions are used instead of the optimal solution which have the property that no improvement in any objective is possible without sacrificing in at least one other objective. In this study, we use achievement scalarization to obtain efficient solutions, i.e. treatment plans which are efficient, for the RTP. We adapt the parameters of the achievement scalarization to address a solution in a rectangle that is defined by the bounds on the objective functions. For a given set of bounds on each structure of the treatment volume, the formulation is able to attain a treatment plan that targets the bounds. We tested our approach on 10 locally advanced head-and-neck cancer cases. All of the cases include three tumor volumes, primary tumor, high-risk nodal volume, low-risk nodal volume, and five organs-at-risk (OAR), left and parotids, spinal cord, brain stem, oral cavity. We compare the proposed method with multiobjective solution algorithm from the literature and clinical plans. While satisfying the coverage of the target volumes, the proposed algorithm was able to improve the OAR sparing as much as 35%.
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    A procedure to find discrete representations of the efficient set with specified coverage errors
    (Inst Operations Research Management Sciences, 2003) Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    An important issue in multiple objective mathematical programming is finding discrete representations of the efficient set. Because discrete points can be directly studied by a decision maker, a discrete representation can serve as the solution to the multiple objective problem at hand. However, the discrete representation must be of acceptable quality to ensure that a most-preferred solution identified by a decision maker is of acceptable quality. Recently, attributes for measuring the quality of discrete representations have been proposed. Although discrete representations can be obtained in many different ways, and their quality evaluated afterwards, the ultimate goal should be to find such representations so as to conform to specified quality standards. We present a method that can find discrete representations of the efficient set according to a specified level of quality. The procedure is based on mathematical programming tools and can be implemented relatively easily when the domain of interest is a polyhedron. The nonconvexity of the efficient set is dealt with through a coordinated decomposition approach. We conduct computational experiments and report results.
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    A game theoretic model and empirical analysis of spammer strategies
    (Conference on Email and Anti-Spam, CEAS, 2010) Parameswaran, Manoj; Rui, Huaxia; Whinston, Andrew B.; Department of Business Administration; Sayın, Serpil; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 6755
    Network security problems are deteriorating worldwide, and can potentially undermine the growth of the digital economy and imperil the multitude of innovations that have been a significant driver of economic growth as well as providing increased services to individuals, businesses, and governments. The emergence of botnets as a powerful force undermining security has raised new and important issues. In particular, the difficulty of detection, elimination and prevention of botnets or spam caused thereof on an absolute scale using computing technologies alone have focused attention on studying behavior patterns of botnets and spammers, to help devise better countermeasures. This paper has two objectives; first to introduce a theoretical modeling approach to spammer behavior and derivation of the model, and second, to compare some of the derivations with data that has been collected from blocklist organizations. By making inferences about the blocklist rules, the spammer can strategize to maximize the amount of spam sent, and we find evidence of spammers using multiple strategies. The blocklist can achieve reduction of spam by investigating longer history of a node's behavior instead of focusing on detection alone. While some of the derivations seem consistent with the data there is considerable room for modification and extension of the modeling approach. The paper concludes with suggestion for the extension of the model.
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    Optimal population screening policies for Alzheimer’s disease*
    (Taylor and Francis Inc., 2019) Gürvit, İbrahim Hakan; Department of Business Administration; N/A; Sayın, Serpil; Önen, Zehra; Faculty Member; PhD Student; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 6755; N/A
    Alzheimer’s disease (AD) constitutes a serious societal healthcare issue as the proportion of the aging population increases. There are ongoing discussions about the necessity of screening the population for AD. We investigate optimal population screening policies for AD using Markov Decision Processes (MDPs). The objective function combines quality-adjusted life years and costs. The disease states are identified according to Clinical Dementia Rating (CDR) scores. The screening test in the model is the Mini Mental State Examination (MMSE), a cognitive test that is widely used in clinical practice. A numerical implementation of the MDP model is presented based on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and existing literature. In the baseline case, the optimal outcome is not to employ a population-wide screening program. We conduct extensive sensitivity analyses on several model parameters. Our study reveals that the optimal policy may be sensitive to changes in transition probability estimates. When we focus on transitions that are related to treatment effectiveness, we find that implementing a population screening policy becomes socially optimal when plans that lead to cognitive ability stabilization or improvement become available.