@article {1536, title = {nTreeClus: A tree-based sequence encoder for clustering categorical series}, journal = {Neurocomputing}, volume = {494}, year = {2022}, pages = {224-241}, abstract = {

The overwhelming presence of categorical/sequential data in diverse domains emphasizes the importance of sequence mining. The challenging nature of sequences proves the need for continuing research to find a more accurate and faster approach providing a better understanding of their (dis) similarities. This paper proposes a new Model-based approach for clustering sequence data, namely nTreeClus. The proposed method deploys Tree-based Learners, k-mers, and autoregressive models for categorical time series, culminating with a novel numerical representation of the categorical sequences. Adopting this new representation, we cluster sequences, considering the inherent patterns in categorical time series. Accordingly, the model showed robustness to its parameter. Under different simulated scenarios, nTreeClus improved the baseline methods for various internal and external cluster validation metrics for up to 10.7\% and 2.7\%, respectively. The empirical evaluation using synthetic and real datasets, protein sequences, and categorical time series showed that nTreeClus is competitive or superior to most state-of-the-art algorithms.

}, keywords = {Categorical time series, Model-based clustering, Pattern recognition, Sequence mining, Tree-based learning}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2022.04.076}, url = {https://www.sciencedirect.com/science/article/pii/S0925231222004611}, author = {Hadi Jahanshahi and Mustafa Gokce Baydogan} } @article {1422, title = {A new feature-based time series classification method by using scale-space extrema}, journal = {Engineering Science and Technology, an International Journal}, year = {2021}, abstract = {

Time series data mining has received significant attention over the past decade, and many approaches have focused on classification tasks where the goal is to define the label of a test time series, given labeled training data. Time series classification approaches can be broadly grouped into two categories as instance-based and feature-based methods. Instance-based approaches utilize similarity information in a nearest-neighbor setting to classify time series data. Although approaches from this category provide accurate results, their performance degrades with long and noisy time series. On the other hand, feature-based approaches extract features to deal with the limitations of instance-based approaches; however, these approaches work with predefined features and may not be successful in certain classification problems. This study proposes a time series classification approach that benefits from both scale-space theory and bag-of-features technique. The method starts with finding the scale-space extrema points (i.e. key points) of each time series according to the SiZer (SIgnificant ZERo crossings of the derivatives) method, and then proceeds to create the local features set around these points. After extraction of the local features from each key point, a bag-of-features representation for each time series is constructed as the summary of the key point characteristics. We evaluate the success of the proposed representation on time series classification problems from various domains. Our experimental results show that our proposal provides competitive results compared to widely used approaches in the literature.

}, keywords = {Bag-of-Features Technique, Feature-based Classification, Scale-Space Theory, SiZer, time series classification}, issn = {2215-0986}, doi = {https://doi.org/10.1016/j.jestch.2021.03.017}, url = {https://www.sciencedirect.com/science/article/pii/S2215098621000823}, author = {Tayip Altay and Mustafa Gokce Baydogan} } @article {1453, title = {Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics}, journal = {European Journal of Operational Research}, volume = {286}, year = {2020}, pages = {49-62}, abstract = {

Operating 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.

}, keywords = {Heuristics, Operating room planning, OR in health services, Scheduling, Stochastic optimization}, issn = {0377-2217}, doi = {https://doi.org/10.1016/j.ejor.2020.03.002}, url = {https://www.sciencedirect.com/science/article/pii/S0377221720302162}, author = {Taghi Khaniyev and Enis Kay{\i}{\c s} and Refik G{\"u}ll{\"u}} } @article {1173, title = {A new multi-objective mathematical model for the high-level synthesis of integrated circuits}, journal = {Applied Mathematical Modelling}, volume = {40}, year = {2016}, pages = {2274{\textendash}2290}, author = {Necati Aras and Yurdakul, Arda} } @article {demange2013note, title = {A note on the NP-hardness of two matching problems in induced subgrids}, journal = {Discrete Mathematics and Theoretical Computer Science}, volume = {15}, number = {2}, year = {2013}, pages = {233{\textendash}242}, author = {Demange, Marc and Ekim, Tinaz and others} } @conference {dingecc2012new, title = {New control variates for L{\'e}vy process models}, booktitle = {Proceedings of the Winter Simulation Conference}, year = {2012}, pages = {15}, publisher = {Winter Simulation Conference}, organization = {Winter Simulation Conference}, author = {Dinge{\c c}, Kemal Din{\c c}er and Wolfgang H{\"o}rmann} } @article {kayics2008note, title = {A note on the can-order policy for the two-item stochastic joint-replenishment problem}, journal = {IIE Transactions}, volume = {40}, number = {1}, year = {2008}, pages = {84{\textendash}92}, publisher = {Taylor \& Francis}, author = {Kayi{\c s}, Enis and Taner Bilgi{\c c} and Karabulut, Deniz} } @article {aras2007new, title = {New heuristic methods for the capacitated multi-facility Weber problem}, journal = {Naval Research Logistics (NRL)}, volume = {54}, number = {1}, year = {2007}, pages = {21{\textendash}32}, publisher = {Wiley Online Library}, author = {Necati Aras and I. Kuban Altinel and Orbay, Metin} } @article {Hoermann:2002a, title = {A note on the Performance of the {\textquoteleft}{\textquoteleft}{A}hrens Algorithm{\textquoteright}{\textquoteright}}, journal = {Computing}, volume = {69}, year = {2002}, pages = {83{\textendash}89}, author = {Wolfgang H{\"o}rmann} } @conference {Hauser;Hoermann;Kunst;Lenneis:1994a, title = {A note on generation, estimation and prediction of stationary processes}, booktitle = {Compstat, Proceedings in Computational Statistics}, year = {1994}, pages = {323{\textendash}328}, publisher = {Physica-Verlag}, organization = {Physica-Verlag}, address = {Heidelberg}, author = {Hauser, M.A. and Wolfgang H{\"o}rmann and Kunst, R.M. and Lenneis, J.} } @article {Hoermann:1994a, title = {A Note on the Quality of Random Variates Generated by the Ratio of Uniforms Method}, volume = {4}, number = {1}, year = {1994}, pages = {96{\textendash}106}, keywords = {linear congruential generators, lower bounds, one-dimensional distribution of pseudorandom numbers, ratio of uniforms method, uniform random numbers}, author = {Wolfgang H{\"o}rmann} } @inbook {Hoermann1993, title = {New generators of normal and {P}oisson deviates based on transformed rejection}, booktitle = {Operations Research Proceedings 1992}, year = {1993}, note = {Proc. DGOR-Tagung Aachen Sept. 92}, pages = {334{\textendash}341}, publisher = {Springer}, organization = {Springer}, address = {Berlin}, author = {Wolfgang H{\"o}rmann} } @conference {Afflerbach;Hoermann:1992a, title = {Nonuniform random numbers: A sensitivity analysis for transformation methods}, booktitle = {Lecture Notes in Econom. Math. Systems}, series = {Lect. Notes Econ. Math. Syst.}, volume = {374}, year = {1992}, note = {Proc. Int. Workshop Comput. Intensive Methods Simulation Optimization, Laxenburg/Austria 1990}, pages = {135{\textendash}144}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {New York}, abstract = {

-?-

}, keywords = {-?-}, author = {Afflerbach, Lothar and Wolfgang H{\"o}rmann} }