Person: TEKSAN, Zehra Melis
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Zehra Melis
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TEKSAN
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Conference paperPublication Open Access A Markovian approach for time series prediction for quality control(Elsevier, 2019) Şahin, Ahmet; Sayımlar, Ayşe Dilara; Teksan, Zehra Melis; Albey, Erinç; Industrial Engineering; TEKSAN, Zehra Melis; ALBEY, Erinç; Şahin, Ahmet; Sayımlar, Ayşe DilaraIn this work we aim to predict quality levels of incoming batches of a selected product type to a white goods manufacturer from a third party supplier. We apply a Markov Model that captures the quality level of the incoming batch in order to predict the quality status of the future arrivals. The ultimate aim is to generate reliable predictions for the future incoming batches, so that the manufacturing company could warn its supplier if the predictions indicate a significant deterioration in the quality. Applied methodology is compared to several benchmark approaches and its superior performance is shown using a benchmark dataset from the literature and the dataset provided by the manufacturing company. Proposed algorithm performs better compared to benchmarks in detecting the instances with quality level falling outside the tolerances in the validation data; and proves itself as a promising approach for the company.
ArticlePublication Metadata only An extension to the classical mean–variance portfolio optimization model(Taylor & Francis, 2019-07) Ötken, Çelen Naz; Organ, Zeynel Batuhan; Yıldırım, Elif Ceren; Çamlıca, Mustafa; Cantürk, Volkan Selim; Duman, Ekrem; Teksan, Zehra Melis; Kayış, Enis; Industrial Engineering; TEKSAN, Zehra Melis; KAYIŞ, Enis; Ötken, Çelen Naz; Organ, Zeynel Batuhan; Yıldırım, Elif Ceren; Çamlıca, Mustafa; Cantürk, Volkan Selim; Duman, EkremThe purpose of this study is to find a portfolio that maximizes the risk-adjusted returns subject to constraints frequently faced during portfolio management by extending the classical Markowitz mean-variance portfolio optimization model. We propose a new two-step heuristic approach, GRASP & SOLVER, that evaluates the desirability of an asset by combining several properties about it into a single parameter. Using a real-life data set, we conduct a simulation study to compare our solution to a benchmark (S&P 500 index). We find that our method generates solutions satisfying nearly all of the constraints within reasonable computational time (under an hour), at the expense of a 13% reduction in the annual return of the portfolio, highlighting the effect of introducing these practice-based constraints.ArticlePublication Metadata only Production planning with price-dependent supply capacity(Taylor and Francis, 2016) Teksan, Zehra Melis; Geunes, J.; Industrial Engineering; TEKSAN, Zehra MelisWe consider a production planning problem in which a producer procures an input component for production by offering a price to suppliers. The available supply quantity for the production input depends on the price the producer offers, and this supply level constrains production output. The producer seeks to meet a set of demands over a finite horizon at a minimum cost, including component procurement costs. We model the problem as a discrete-time production and component supply–pricing planning problem with nonstationary costs, demands, and component supply levels. This leads to a two-level lot-sizing problem with an objective function that is neither concave nor convex. Although the most general version of the problem is NP-hard, we provide polynomial-time algorithms for two special cases of the model under particular assumptions on the cost structure. We then apply the resulting algorithms heuristically to the more general problem version and provide computational results that demonstrate the high performance quality of the resulting heuristic solution methods.