@article {1540, title = {A branch-and-price algorithm for parallel machine campaign planning under sequence dependent family setups and co-production}, journal = {Computers \& Operations Research}, volume = {135}, year = {2021}, pages = {105430}, doi = {https://doi.org/10.1016/j.cor.2021.105430}, author = {Serkan Kalay and Z C Ta{\c s}k{\i}n} } @article {1539, title = {A Branch-Price-and-Cut Algorithm for Optimal Decoding of LDPC Codes}, journal = {Journal of Global Optimization}, volume = {81}, year = {2021}, pages = {805{\textendash}834}, doi = {https://doi.org/10.1007/s10898-021-01073-4}, author = {Banu Kabakulak and Z C Ta{\c s}k{\i}n and Ali Emre Pusane} } @article {1419, title = {The benefits of target relations: A comparison of multitask extensions and classifier chains}, journal = {Pattern Recognition}, volume = {107}, year = {2020}, pages = {107507}, abstract = {

Multitask (multi-target or multi-output) learning (MTL) deals with simultaneous prediction of several outputs. MTL approaches rely on the optimization of a joint score function over the targets. However, defining a joint score in global models is problematic when the target scales are different. To address such problems, single target (i.e. local) learning strategies are commonly employed. Here we propose alternative tree-based learning strategies to handle the issue with target scaling in global models, and to identify the learning order for chaining operations in local models. In the first proposal, the problems with target scaling are resolved using alternative splitting strategies which consider the learning tasks in a multi-objective optimization framework. The second proposal deals with the problem of ordering in the chaining strategies. We introduce an alternative estimation strategy, minimum error chain policy, that gradually expands the input space using the estimations that approximate to true characteristics of outputs, namely out-of-bag estimations in tree-based ensemble framework. Our experiments on benchmark datasets illustrate the success of the proposed multitask extension of trees compared to the decision trees with de facto design especially for datasets with large number of targets. In line with that, minimum error chain policy improves the performance of the state-of-the-art chaining policies.

}, keywords = {Classifier chains, Ensemble learning, Multi-objective trees, Multitask learning, Stacking}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2020.107507}, url = {https://www.sciencedirect.com/science/article/pii/S0031320320303101}, author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan} } @article {1426, title = {A branch-and-cut algorithm for a bipartite graph construction problem in digital communication systems}, journal = {Networks}, volume = {75}, year = {2020}, pages = {137-157}, abstract = {

Abstract We study a bipartite graph (BG) construction problem that arises in digital communication systems. In a digital communication system, information is sent from one place to another over a noisy communication channel using binary symbols (bits). The original information is encoded by adding redundant bits, which are then used to detect and correct errors that may have been introduced during transmission. Harmful structures, such as small cycles, severely deteriorate the error correction capability of a BG. We introduce an integer programming formulation to generate a BG for a given smallest cycle length. We propose a branch-and-cut algorithm for its solution and investigate the structural properties of the problem to derive valid inequalities and variable fixing rules. We also introduce heuristics to obtain feasible solutions for the problem. The computational experiments show that our algorithm can generate BGs without small cycles in an acceptable amount of time for practically relevant dimensions.

}, keywords = {bipartite graphs, branch-and-cut algorithm, integer programming, symmetry, telecommunications, valid inequalities}, doi = {https://doi.org/10.1002/net.21914}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/net.21914}, author = {Banu Kabakulak and Z C Ta{\c s}k{\i}n and Pusane, Ali E.} } @article {1440, title = {Branch and bound algorithms for solving the multi-commodity capacitated multi-facility Weber problem}, journal = {Annals of Operations Research}, volume = {279}, year = {2019}, pages = {1{\textendash}42}, author = {Aky{\"u}z, M Hakan and {\"O}ncan, Temel and I. Kuban Altinel} } @inbook {1186, title = {Bilevel Models on the Competitive Facility Location Problem}, booktitle = {Spatial Interaction Models}, year = {2017}, pages = {1{\textendash}19}, publisher = {Springer}, organization = {Springer}, author = {Necati Aras and K{\"u}{\c c}{\"u}kayd{\i}n, Hande} } @article {1130, title = {Branch-cut-price algorithms for solving a class of search problems on general graphs}, journal = {Networks}, volume = {70}, year = {2017}, pages = {4-18}, doi = {http://doi.org/10.1002/net.21740}, author = {Z C Ta{\c s}k{\i}n and J. Cole Smith} } @article {1460, title = {Baker- Lin-Huang Type Bivariate Distributions Based on Order Statistics}, journal = {Communications in Statistics - Theory and Methods}, volume = {43}, year = {2014}, pages = {1992-2006}, abstract = {

Baker (2008) introduced a new class of bivariate distributions based on distributions of order statistics from two independent samples of size n. Lin and Huang (2010) discovered an important property of Baker{\textquoteright}s distribution and showed that the Pearson{\textquoteright}s correlation coefficient for this distribution converges to maximum attainable value, i.e., the correlation coefficient of the Fr{\'e}chet upper bound, as n increases to infinity. Bairamov and Bayramoglu (2013) investigated a new class of bivariate distributions constructed by using Baker{\textquoteright}s model and distributions of order statistics from dependent random variables, allowing higher correlation than that of Baker{\textquoteright}s distribution. In this article, a new class of Baker{\textquoteright}s type bivariate distributions with high correlation are constructed based on distributions of order statistics by using an arbitrary continuous copula instead of the product copula.

}, doi = {10.1080/03610926.2013.775301}, url = {https://doi.org/10.1080/03610926.2013.775301}, author = {K{\"o}n{\"u}l Bayramo{\u g}lu Kavlak and I. Bayramoglu (Bairamov)} } @article {aksen2014bilevel, title = {A bilevel partial interdiction problem with capacitated facilities and demand outsourcing}, journal = {Computers \& Operations Research}, volume = {41}, year = {2014}, pages = {346{\textendash}358}, publisher = {Pergamon}, author = {Aksen, Deniz and {\c S}eng{\"u}l Akca, Sema and Necati Aras} } @article {ekim2014block, title = {Block decomposition approach to compute a minimum geodetic set}, journal = {RAIRO-Operations Research}, volume = {48}, number = {04}, year = {2014}, pages = {497{\textendash}507}, publisher = {EDP Sciences}, author = {Ekim, Tinaz and Erey, Aysel} } @article {33, title = {A Bag-of-Features Framework to Classify Time Series}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {35}, year = {2013}, pages = {2796-2802}, abstract = {

Time series classification is an important task with many challenging applications. A nearest neighbor (NN) classifier with dynamic time warping (DTW) distance is a strong solution in this context. On the other hand, feature-based approaches have been proposed as both classifiers and to provide insight into the series, but these approaches have problems handling translations and dilations in local patterns. Considering these shortcomings, we present a framework to classify time series based on a bag-of-features representation (TSBF). Multiple subsequences selected from random locations and of random lengths are partitioned into shorter intervals to capture the local information. Consequently, features computed from these subsequences measure properties at different locations and dilations when viewed from the original series. This provides a feature-based approach that can handle warping (although differently from DTW). Moreover, a supervised learner (that handles mixed data types, different units, etc.) integrates location information into a compact codebook through class probability estimates. Additionally, relevant global features can easily supplement the codebook. TSBF is compared to NN classifiers and other alternatives (bag-of-words strategies, sparse spatial sample kernels, shapelets). Our experimental results show that TSBF provides better results than competitive methods on benchmark datasets from the UCR time series database.

}, keywords = {codebook, feature extraction, supervised learning}, issn = {0162-8828}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.72}, author = {Mustafa Gokce Baydogan and George Runger and Eugene Tuv} } @article {aksen2013bilevel, title = {A bilevel p-median model for the planning and protection of critical facilities}, journal = {Journal of Heuristics}, volume = {19}, number = {2}, year = {2013}, pages = {373{\textendash}398}, publisher = {Springer US}, author = {Aksen, Deniz and Necati Aras and Piyade, Nuray} } @article {aksen2012bilevel, title = {A bilevel fixed charge location model for facilities under imminent attack}, journal = {Computers \& Operations Research}, volume = {39}, number = {7}, year = {2012}, pages = {1364{\textendash}1381}, publisher = {Pergamon}, author = {Aksen, Deniz and Necati Aras} } @article {aksen2010budget, title = {The budget constrained r-interdiction median problem with capacity expansion}, journal = {Central European Journal of Operations Research}, volume = {18}, number = {3}, year = {2010}, pages = {269{\textendash}291}, publisher = {Springer-Verlag}, author = {Aksen, Deniz and Piyade, Nuray and Necati Aras} } @article {4363814, title = {{Binary integer programming formulation and heuristics for differentiated coverage in heterogeneous sensor networks}}, journal = {Computer Networks}, volume = {52}, year = {2008}, pages = {2419{\textendash}2431}, doi = {10.1016/j.comnet.2008.05.002}, author = {I. Kuban Altinel and Necati Aras and Evren G{\"u}ney and Cem Ersoy} } @conference {Hoermann;Leydold:2006a, title = {Black-Box Algorithms for Sampling from Continuous Distributions}, booktitle = {Proceedings of the 2006 Winter Simulation Conference}, year = {2006}, pages = {129{\textendash}136}, author = {Wolfgang H{\"o}rmann and Leydold, Josef} } @conference {Leydold;Hoermann:2000a, title = {Black Box Algorithms for Generating Non-Uniform Continuous Random Variates}, booktitle = {COMPSTAT 2000. Short Communications and Posters}, year = {2000}, pages = {53{\textendash}54}, publisher = {Statistics Netherlands}, organization = {Statistics Netherlands}, author = {Leydold, Josef and Wolfgang H{\"o}rmann} } @article {1492, title = {BATCH SEQUENCING}, journal = {IIE Transactions}, volume = {24}, year = {1992}, pages = {73-83}, abstract = {

Consider the single machine scheduling problem where there are a number of part types to be processed. A part type is defined as follows: Two parts are of the same part type if the machine does not require a setup in between the processing of these parts. The problem investigated in this paper is to find a sequence of batches of parts (if there are any) where all the requirements for parts are met. A heuristic and an exact algorithm are developed, and computational analysis is performed to measure the performance of the heuristic. The time complexity function of the heuristic is O(n2), and the exact algorithm runs in polynomial time given a fixed upper bound on the number of setups.

}, doi = {10.1080/07408179208964235}, url = {https://doi.org/10.1080/07408179208964235}, author = {Ali Tamer {\"U}nal and Ali S. Kiran} }