Person: KAYIŞ, Enis
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Enis
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KAYIŞ
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ArticlePublication Metadata only Delegation vs. control of component procurement under asymmetric cost information and simple contracts(Informs, 2013) Kayış, Enis; Erhun, F.; Plambeck, E. L.; Industrial Engineering; KAYIŞ, EnisA manufacturer must choose whether to delegate component procurement to her tier 1 supplier or control it directly. Because of information asymmetry about suppliers’ production costs and the use of simple quantity discount or price-only contracts, either delegation or control can yield substantially higher expected profit for the manufacturer. Delegation tends to outperform control when (1) the manufacturer is uncertain about the tier 1 supplier’s cost and believes that it is likely to be high; (2) the manufacturer and the tier 1 supplier know the tier 2 supplier’s cost or at least that it will be high; (3) the manufacturer has an alternative to engaging the tier 1 and tier 2 suppliers, such as in-house production; and (4) the firms use price-only contracts as opposed to quantity discount contracts. These results shed light on practices observed in the electronics industry.Conference paperPublication Open Access Effective training methods for automatic musical genre classification(SciTePress, 2019) Atsız, Eren; Albey, Erinç; Kayış, Enis; Industrial Engineering; Hammoudi, S.; Quix, C.; Bernardino, J.; ALBEY, Erinç; KAYIŞ, EnisMusical genres are labels created by human and based on mutual characteristics of songs, which are also called musical features. These features are key indicators for the content of the music. Rather than predictions by human decisions, developing an automatic solution for genre classification has been a significant issue over the last decade. In order to have automatic classification for songs, different approaches have been indicated by studying various datasets and part of songs. In this paper, we suggest an alternative genre classification method based on which part of songs have to be used to have a better accuracy level. Wide range of acoustic features are obtained at the end of the analysis and discussed whether using full versions or pieces of songs is better. Both alternatives are implemented and results are compared. The best accuracy level is 55% while considering the full version of songs. Besides, additional analysis for Turkish songs is also performed. All analysis, data, and results are visualized by a dynamic dashboard system, which is created specifically for the study.ArticlePublication Metadata only Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics(Elsevier, 2020-10-01) Khaniyev, T.; Kayış, Enis; Güllü, R.; Industrial Engineering; KAYIŞ, EnisOperating rooms are units of particular interest in hospitals as they constitute more than 40% of total expenses and revenues. Managing operating rooms is challenging due to conflicting priorities and preferences of various stakeholders and the inherent uncertainty of surgery durations. In this study, we consider the next-day scheduling problem of a hospital operating room. Given the list and the sequence of non-identical surgeries to be performed in the next day, one needs to determine the scheduled durations of surgeries where the actual duration of each surgery is uncertain. Our objective is to minimize the weighted sum of expected patient waiting times, room idle time and overtime. First, we provide a reformulation of the objective function in terms of auxiliary functions with a recursive pattern that enables exact analysis of the optimal surgery durations at the expense of high CPU time. Next, we develop and analyze simple-to-use and close-to-optimal scheduling heuristics motivated by practice, for the OR managers to deploy in the field. Our proposed hybrid heuristic attains 1.22% average performance gap and worst average optimality gap of 2.77%. Our solution is easy to implement as it does not require any advanced optimization tool, which is the reality of many operating room environments.Conference paperPublication Metadata only A longitudinal model for song popularity prediction(SciTePress, 2021) Çimen, Ahmet; Kayış, Enis; Industrial Engineering; KAYIŞ, Enis; Çimen, AhmetUsage of new generation music streaming platforms such as Spotify and Apple Music has increased rapidly in the last years. Automatic prediction of a song's popularity is valuable for these firms which in turn translates into higher customer satisfaction. In this study, we develop and compare several statistical models to predict song popularity by using acoustic and artist-related features. We compare results from two countries to understand whether there are any cultural differences for popular songs. To compare the results, we use weekly charts and songs' acoustic features as data sources. In addition to acoustic features, we add acoustic similarity, genre, local popularity, song recentness features into the dataset. We applied Flexible Least Squares (FLS) method to estimate song streams and observe time-varying regression coefficients using a quadratic program. FLS method predicts the number of weekly streams of a song using the acoustic features and the additional features in the dataset while keeping weekly model differences as small as possible. Results show that the significant changes in the regression coefficients may reflect the changes in the music tastes of the countries.ArticlePublication Metadata only Single item periodic review inventory control with sales dependent stochastic return flows(Elsevier, 2023-01) Gökbayrak, Esra; Kayış, Enis; Industrial Engineering; KAYIŞ, Enis; Gökbayrak, EsraRetailers have to deal with increasing levels of product returns as the shares of e-commerce sales soars. With this increase, it is no longer feasible to dispatch returned products to outlets or landfills, hence retailers must re-evaluate them both to maximize profit and to minimize their environmental impact. Our objective is to study a retailer's optimal inventory control policy under product returns to maximize expected profit which is the sales revenue minus the procurement, backorder, holding, and salvage costs incurred in a finite horizon. We model a period's returns to be stochastically dependent on the previous period's sales quantity. Using dynamic programming formulation, we solve for the optimal periodic review inventory policy and provide structural results on the optimal policy of the final period. Through numerical studies, we show that incorporating detailed sales-dependent returns could increase a retailer's expected profit by 23%. Ignoring this dependency in determining the optimal inventory policy results with increased order frequency, higher levels of backorders and more leftovers which could eventually end up in a landfill, but above all could lead to a significant overestimation of the resulting profit.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.Conference paperPublication Metadata only Designing an efficient gradient descent based heuristic for clusterwise linear regression for large datasets(Springer, 2021) Kayış, Enis; Industrial Engineering; KAYIŞ, EnisMultiple linear regression is the method of quantifying the effects of a set of independent variables on a dependent variable. In clusterwise linear regression problems, the data points with similar regression estimates are grouped into the same cluster either due to a business need or to increase the statistical significance of the resulting regression estimates. In this paper, we consider an extension of this problem where data points belonging to the same category should belong to the same partition. For large datasets, finding the exact solution is not possible and many heuristics requires an exponentially increasing amount of time in the number of categories. We propose variants of gradient descent based heuristic to provide high-quality solutions within a reasonable time. The performances of our heuristics are evaluated across 1014 simulated datasets. We find that the comparative performance of the base gradient descent based heuristic is quite good with an average percentage gap of 0.17 % when the number of categories is less than 60. However, starting with a fixed initial partition and restricting cluster assignment changes to be one-directional speed up heuristic dramatically with a moderate decrease in solution quality, especially for datasets with a multiple number of predictors and a large number of datasets. For example, one could generate solutions with an average percentage gap of 2.81 % in one-tenth of the time for datasets with 400 categories.ArticlePublication Metadata only A robust estimation model for surgery durations with temporal, operational, and surgery team effects(Springer Science+Business Media, 2015-09) Kayış, Enis; Khaniyev, T. T.; Suermondt, J.; Sylvester, K.; Industrial Engineering; KAYIŞ, EnisFor effective operating room (OR) planning, surgery duration estimation is critical. Overestimation leads to underutilization of expensive hospital resources (e.g., OR time) whereas underestimation leads to overtime and high waiting times for the patients. In this paper, we consider a particular estimation method currently in use and using additional temporal, operational, and staff-related factors provide a statistical model to adjust these estimates for higher accuracy. The results show that our method increases the accuracy of the estimates, in particular by reducing large errors. For the 8093 cases we have in our data, our model decreases the mean absolute deviation of the currently used scheduled duration (42.65 ± 0.59 minutes) by 1.98 ± 0.28 minutes. For the cases with large negative errors, however, the decrease in the mean absolute deviation is 20.35 ± 0.74 minutes (with a respective increase of 0.89 ± 0.66 minutes in large positive errors). We find that not only operational and temporal factors, but also medical staff and team experience related factors (such as number of nurses and the frequency of the medical team working together) could be used to improve the currently used estimates. Finally, we conclude that one could further improve these predictions by combining our model with other good prediction models proposed in the literature. Specifically, one could decrease the mean absolute deviation of 39.98 ± 0.58 minutes obtained via the method of Dexter et al (Anesth Analg 117(1):204–209, 2013) by 1.02 ± 0.21 minutes by combining our method with theirs.ArticlePublication Metadata only The effect of few historical data on the performance of sample average approximation method for operating room scheduling(Wiley, 2023-01) Göktürk, Elvin Çoban; Kayış, Enis; Dexter, F.; Industrial Engineering; GÖKTÜRK, Elvin Çoban; KAYIŞ, EnisWe model the scheduling problem of a single operating room for outpatient surgery, with uncertain case durations and an objective function comprising waiting time, idle time, and overtime costs. This stochastic scheduling problem has been studied in diverse forms. One of the most common approaches used is the sample average approximation (SAA). Our contribution is to study the use of SAA to solve this problem under few historical data using families of log t distributions with varying degrees of freedom. We analyze the results of the SAA method in terms of optimality convergence, the effect of the number of scenarios, and average computational time. Given the case sequence, computational results demonstrate that SAA with an adequate number of scenarios performs close to the exact method. For example, we find that the optimality gap, in units of proportional weighted time, is relatively small when 500 scenarios are used: 99% of the instances have an optimality gap of less than 2.6 7% (1.74%, 1.23%) when there are 3 (9, many) historical samples. Increasing the number of SAA scenarios improves performance, but is not critical when the case sequence is given. However, choosing the number of SAA scenarios becomes critical when the same method is used to choose among sequencing heuristics when there are few historical data. For example, when there are only three (nine, many) historical samples, 99% of the instances have less than 25.38% (13.15%, 6.87%) penalty in using SAA with 500 scenarios to choose the best sequencing heuristic.Conference paperPublication Metadata only A gradient descent based heuristic for solving regression clustering problems(SciTePress, 2020) Kayış, Enis; Industrial Engineering; KAYIŞ, EnisRegression analysis is the method of quantifying the effects of a set of independent variables on a dependent variable. In regression clustering problems, the data points with similar regression estimates are grouped into the same cluster either due to a business need or to increase the statistical significance of the resulting regression estimates. In this paper, we consider an extension of this problem where data points belonging to the same level of another partitioning categorical variable should belong to the same partition. Due to the combinatorial nature of this problem, an exact solution is computationally prohibitive. We provide an integer programming formulation and offer gradient descent based heuristic to solve this problem. Through simulated datasets, we analyze the performance of our heuristic across a variety of different settings. In our computational study, we find that our heuristic provides remarkably better solutions than the benchmark method within a reasonable time. Albeit the slight decrease in the performance as the number of levels increase, our heuristic provides good solutions when each of the true underlying partition has a similar number of levels.