@article {1602, title = {Column generation-based prototype learning for optimizing area under the receiver operating characteristic curve}, journal = {European Journal of Operational Research}, volume = {314}, year = {2024}, pages = {297{\textendash}307}, author = {Erhan C Ozcan and Berk G{\"o}rg{\"u}l{\"u} and Mustafa Gokce Baydogan} } @article {1538, title = {Multiple instance classification via quadratic programming}, journal = {Journal of Global Optimization}, year = {2022}, pages = {1{\textendash}32}, author = {Emel {\c S}eyma K{\"u}{\c c}{\"u}ka{\c s}c{\i} and Mustafa Gokce Baydogan and Z C Ta{\c s}k{\i}n} } @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 {1587, title = {A reference set based many-objective co-evolutionary algorithm with an application to the knapsack problem}, journal = {European Journal of Operational Research}, volume = {300}, year = {2022}, pages = {405{\textendash}417}, doi = {10.1016/j.ejor.2021.10.033}, url = {https://doi.org/10.1016/j.ejor.2021.10.033}, author = {H. Mert Sahinkoc and {\"U}mit Bilge} } @article {1537, title = {Semi-supervised extensions of multi-task tree ensembles}, journal = {Pattern Recognition}, volume = {123}, year = {2022}, pages = {108393}, abstract = {

Scale inconsistency is a widely encountered issue in multi-output learning problems. Specifically, target sets with multiple real valued or a mixture of categorical and real valued variables require addressing the scale differences to obtain predictive models with sufficiently good performance. Data transformation techniques are often employed to solve that problem. However, these operations are susceptible to different shortcomings such as changing the statistical properties of the data and increase the computational burden. Scale differences also pose problem in semi-supervised learning (SSL) models as they require processing of unsupervised information where distance measures are commonly employed. Classical distance metrics can be criticized as they lose efficiency when variables exhibit type or scale differences, too. Besides, in higher dimensions distance metrics cause problems due to loss of discriminative power. This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. We propose use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data. We update classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies. We define proximity based clustering indicators and extend the supervised model with unsupervised criteria. Our experiments show that proposed method significantly outperforms its benchmark learning model that is predictive clustering trees.

}, keywords = {Ensemble learning, Multi-objective trees, Multi-task learning, Semi-supervised learning, Totally randomized trees}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108393}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321005549}, author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan} } @article {1423, title = {An ensemble-based semi-supervised feature ranking for multi-target regression problems}, journal = {Pattern Recognition Letters}, volume = {148}, year = {2021}, pages = {36-42}, abstract = {

This study focuses on semi-supervised feature ranking (FR) applications for multi-target regression problems (MTR). As MTRs require prediction of several targets, we use a learning model that includes target interrelations via multi-objective trees. In processing the features for a semi-supervised learning model, transformation or scaling operations are usually required. To resolve this issue, we create a dissimilarity matrix via totally randomized trees to process the unsupervised information. Besides, we treat the split score function as a vector to make it suitable for considering each criterion regardless of their scales. We propose a semi-supervised FR scheme embedded to multi-objective trees that takes into account target and feature contributions simultaneously. Proposed FR score is compared with the state-of-the-art multi-target FR strategies via statistical analyses. Experimental studies show that proposed score significantly improves the performance of a recent tree-based and competitive multi-target learning model, i.e. predictive clustering trees. In addition, proposed approach outperforms its benchmarks when the available labelled data increase.

}, keywords = {Feature ranking, Multi-target regression, Semi-supervised learning}, issn = {0167-8655}, doi = {https://doi.org/10.1016/j.patrec.2021.04.025}, url = {https://www.sciencedirect.com/science/article/pii/S0167865521001641}, author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan} } @article {1420, title = {Explainable boosted linear regression for time series forecasting}, journal = {Pattern Recognition}, volume = {120}, year = {2021}, pages = {108144}, abstract = {

Time series forecasting involves collecting and analyzing past observations to develop a model to extrapolate such observations into the future. Forecasting of future events is important in many fields to support decision making as it contributes to reducing the future uncertainty. We propose explainable boosted linear regression (EBLR) algorithm for time series forecasting, which is an iterative method that starts with a base model, and explains the model{\textquoteright}s errors through regression trees. At each iteration, the path leading to highest error is added as a new variable to the base model. In this regard, our approach can be considered as an improvement over general time series models since it enables incorporating nonlinear features by residual explanation. More importantly, use of the single rule that contributes to the error most enables access to interpretable results. The proposed approach extends to probabilistic forecasting through generating prediction intervals based on the empirical error distribution. We conduct a detailed numerical study with EBLR and compare against various other approaches. We observe that EBLR substantially improves the base model performance through extracted features, and provide a comparable performance to other well established approaches. The interpretability of the model predictions and high predictive accuracy of EBLR makes it a promising method for time series forecasting.

}, keywords = {ARIMA, Decision trees, Linear regression, Probabilistic forecasting, Time series regression}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108144}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321003319}, author = {Igor Ilic and Berk G{\"o}rg{\"u}l{\"u} and Mucahit Cevik and Mustafa Gokce Baydogan} } @article {1535, title = {Learning prototypes for multiple instance learning.}, journal = {Turkish Journal of Electrical Engineering \& Computer Sciences}, volume = {29}, year = {2021}, author = {{\"O}zg{\"u}r Emre Sivrikaya and Mert Y{\"u}kselg{\"o}n{\"u}l and Mustafa Gokce Baydogan} } @article {1424, title = {A linear programming approach to multiple instance learning}, journal = {Turkish Journal of Electrical Engineering \& Computer Sciences}, volume = {29}, year = {2021}, pages = {2186{\textendash}2201}, author = {Emel {\c S}eyma K{\"u}{\c c}{\"u}ka{\c s}c{\i} and Mustafa Gokce Baydogan and Z C Ta{\c s}k{\i}n} } @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 {1421, title = {Randomized trees for time series representation and similarity}, journal = {Pattern Recognition}, volume = {120}, year = {2021}, pages = {108097}, abstract = {

Most of the temporal data mining tasks require representations to capture important characteristics of time series. Representation learning is challenging when time series differ in distributional characteristics and/or show irregularities such as varying lengths and missing observations. Moreover, when time series are multivariate, interactions between variables should be modeled efficiently. This study proposes a unified, flexible time series representation learning framework for both univariate and multivariate time series called Rand-TS. Rand-TS models density characteristics of each time series as a time-varying Gaussian distribution using random decision trees and embeds density information into a sparse vector. Rand-TS can work with time series of various lengths and missing observations, furthermore, it allows using customized features. We illustrate the classification performance of Rand-TS on 113 univariate, 19 multivariate along with 15 univariate time series with varying lengths from UCR database. The results show that in addition to its flexibility, Rand-TS provides competitive classification performance.

}, keywords = {Classification, Random trees, Representation learning, Time series}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108097}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321002843}, author = {Berk G{\"o}rg{\"u}l{\"u} and Mustafa Gokce Baydogan} } @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 {1449, title = {Modeling the pharmacodynamics of nandrolone doping drug and implications for anti-doping testing}, journal = {System Dynamics Review}, volume = {36}, year = {2020}, pages = {467-496}, abstract = {

Abstract We model the pathways of nandrolone in the body, an anabolic steroid widely used as a performance enhancing drug (PED). The model generates the dynamics of nandrolone and its metabolites. PED tests check for the presence of a primary metabolite of nandrolone, 19-NA in urine. To cheat in these tests, PED users typically use inhibitors that reduce the urinary concentration of 19-NA. One such inhibitor is finasteride. Finasteride{\textquoteright}s main effect in the body is the inhibition of reductase enzymes that turn nandrolone into its metabolite 19-NA. To capture this effect, we include structures for finasteride and reductase enzymes in the model. The model is tested by fundamental structure validity tests. We also show that the model behavior is consistent with experimental data in the literature. We finally investigate the potential ways by which the drug users may cheat in PED tests and make suggestions for improved testing as counter-measures. {\textcopyright} 2021 System Dynamics Society

}, doi = {https://doi.org/10.1002/sdr.1672}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sdr.1672}, author = {Sahin, {\"O}zge and Feyyaz {\c S}ent{\"u}rk and Yaman Barlas and Yasarcan, Hakan} } @article {1586, title = {A robust disaster preparedness model for effective and fair disaster response}, journal = {European Journal of Operational Research}, volume = {280}, year = {2020}, pages = {479{\textendash}494}, doi = {10.1016/j.ejor.2019.07.029}, url = {https://doi.org/10.1016/j.ejor.2019.07.029}, author = {G{\"o}kalp Erbeyo{\u g}lu and {\"U}mit Bilge} } @inbook {1585, title = {Solution of a Real-Life Vehicle Routing Problem with Meal Breaks and Shifts}, booktitle = {Green Transportation and New Advances in Vehicle Routing Problems}, year = {2020}, pages = {131{\textendash}157}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, doi = {10.1007/978-3-030-45312-1_5}, url = {https://doi.org/10.1007/978-3-030-45312-1_5}, author = {Ci{\u g}dem Karademir and {\"U}mit Bilge and Necati Aras and G{\"o}kay Burak Akku{\c s} and G{\"o}ksu {\"O}znergiz and Onur Do{\u g}an} } @article {1415, title = {Classification of generic system dynamics model outputs via supervised time series pattern discovery}, journal = {Turkish Journal of Electrical Engineering \& Computer Sciences}, volume = {27}, year = {2019}, pages = {832{\textendash}846}, author = {Edali, Mert and Mustafa Gokce Baydogan and G{\"o}nen{\c c} Y{\"u}cel} } @article {1433, title = {A column generation heuristic for {VMAT} planning with adaptive {CVaR} constraints}, journal = {Physics in Medicine {\&} Biology}, volume = {64}, year = {2019}, month = {oct}, pages = {205024}, abstract = {

In this study we develop an efficient computational procedure that generates medically acceptable treatment plans for volumetric modulated arc therapy with constant gantry speed. Our proposed method is a column generation heuristic based on a mixed integer linear programming model, where the objective function contains minimization of total monitor unit of the treatment plan and dose-volume requirements are included as conditional value-at-risk constraints. Our heuristic generates a full treatment arc for the restricted master problem and calibrates the right hand side parameters of the conditional value-at-risk constraints in the first phase. In the second phase, this initial solution is improved by performing column generation. This is a fully automated procedure and produces treatment plans in a single call without any human intervention. We evaluate its performance on real prostate cancer data by comparing the quality of the generated plans with those obtained by a widely used commercial treatment planning system. Our analysis shows that the results are promising, and the generated plans satisfy the prescription restrictions and require fewer monitor units on average compared to the ones obtained using Eclipse.

}, doi = {10.1088/1361-6560/ab416e}, url = {https://doi.org/10.1088/1361-6560/ab416e}, author = {P{\i}nar Dursun and Z C Ta{\c s}k{\i}n and I. Kuban Altinel and Hatice Bilge and Nazmiye D{\"o}nmez Kesen and Murat Okutan and Ethem Nezih Oral} } @article {1418, title = {Estimation of Influence Distance of Bus Stops Using Bus GPS Data and Bus Stop Properties}, journal = {IEEE Transactions on Intelligent Transportation Systems}, volume = {20}, year = {2019}, pages = {4635-4642}, doi = {10.1109/TITS.2019.2909645}, author = {Gokasar, Ilgin and Cetinel, Yigit and Mustafa Gokce Baydogan} } @article {1445, title = {SLA-aware optimal resource allocation for service-oriented networks}, journal = {Future Generation Computer Systems}, volume = {101}, year = {2019}, pages = {959-974}, abstract = {

The popularity of traditional network services and web content is succeeded by the recent trend in customized services proliferated by the smart devices and gadgets. Fall-risk assessment, augmented reality, ECG (electrocardiography) monitoring, virtual reality-based gaming and similar services are driven through data generated by multi-modal sensors embedded in the end-user equipment. These services may possess varying characteristics and requirements represented with performance metrics and Quality of Service (QoS) parameters. Even though the small form-factor end-user gadgets are getting powerful in terms of resource capacity, they are still incapable of executing complex routines, and thus these tasks should be offloaded to a remote machine. Service-Centric Networks (SCN) focus on delivering customized services to the users in a location-independent fashion. This is in parallel with previous vision put forward by the Information-Centric Networks (ICN) and Content Delivery Networks (CDN), which aim to enhance the end-user experience. The novel set of services for complementing the daily activities of the end-users mostly depicts a latency-intolerant attribute which ultimately calls for a full-fledged resource allocation scheme. Within this context, both computation and networking resources should be allocated optimally, and task assignments should be handled precisely for following the requirements specified by the Service Level Agreements (SLAs). This paper initially presents and discusses problem definitions that should be addressed by the service-centric multi-tier computing architecture that is composed of edge, metro, and cloud servers. In order to achieve this objective, an SLA-aware optimal resource allocation and task assignment model for service-oriented networks is proposed. This optimization model is based on a nonlinear delay formulation for accommodating service-centric network scenarios under various conditions. It is then reshaped as a mixed-integer linear model through piecewise linear approximation. Additionally, a heuristic implementation is presented to address the time and space complexities of the problem for which the aforementioned optimization models remain ineffective. Performance evaluation results show that the proposed solutions are able to find a good allocation of resources while taking the requirements of the services into account.

}, keywords = {Cloud computing, Edge computing, Network optimization, Service-centric networks, Task offloading}, issn = {0167-739X}, doi = {https://doi.org/10.1016/j.future.2019.07.050}, url = {https://www.sciencedirect.com/science/article/pii/S0167739X19306430}, author = {Ahmet Cihat Bakt{\i}r and Bet{\"u}l Ahat and Necati Aras and Atay {\"O}zg{\"o}vde and Cem Ersoy} } @article {1157, title = {Autoregressive forests for multivariate time series modeling}, journal = {Pattern Recognition}, volume = {73}, year = {2018}, pages = {202 - 215}, keywords = {Classification, Ensemble learning, Multivariate time series, Time series representation, Vector autoregression}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2017.08.016}, url = {http://www.sciencedirect.com/science/article/pii/S0031320317303291}, author = {Kerem Sinan Tuncel and Mustafa Gokce Baydogan} } @inbook {1451, title = {Credibility, Validity and Testing of Dynamic Simulation Models}, booktitle = {Simulation and Modeling Methodologies, Technologies and Applications}, year = {2018}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

Also called {\textquoteleft}model validity{\textquoteright} testing, model credibility evaluation has always been a controversial issue in any modeling methodology. We briefly discuss why this important notion is so controversial. To this end, we classify major types of models, particularly as they impact the notion of model credibility: i- Purely statistical forecasting (black box) models, and ii- Causal-descriptive policy (transparent) models. We then focus on what makes causal-descriptive model credibility and evaluation unique and quite difficult, compared to short-term forecasting models. One important result is that causal-descriptive model credibility consists of two different aspects: structural and behavioral. In most simulation modeling (particularly system dynamics policy simulation), establishing structure credibility must strictly precede behavior credibility; the latter has no value without the former. We thus discuss Structural tests and output behavior tests for dynamic simulation models separately. Structure tests can further be classified into direct and indirect structure tests. We place special emphasis on indirect structure tests. We also provide a quick overview of recent model testing software developed in SESDYN Laboratory at Bo{\u g}azi{\c c}i University. Finally we discuss model credibility in broader context and some implementation issues.

}, isbn = {978-3-319-69832-8}, author = {Yaman Barlas} } @article {1161, title = {Efficient simulations for a Bernoulli mixture model of portfolio credit risk}, journal = {Annals of Operations Research}, volume = {260}, year = {2018}, pages = {113{\textendash}128}, author = {Ba{\c s}o{\u g}lu, {\.I}smail and Wolfgang H{\"o}rmann and Sak, Halis} } @article {1504, title = {Graphs of Edge-Intersecting Non-Splitting Paths in a Tree: Representations of Holes-Part II}, journal = {Discrete Mathematics and Theoretical Computer Science}, volume = {20}, year = {2018}, pages = {1b{\textendash}1b}, author = {Boyac{\i}, Arman and Ekim, Tinaz and Shalom, Mordechai and Zaks, Shmuel} } @article {1502, title = {The maximum cardinality cut problem in co-bipartite chain graphs}, journal = {Journal of Combinatorial Optimization}, volume = {35}, year = {2018}, month = {Jan}, pages = {250{\textendash}265}, issn = {1382-6905}, author = {Ekim, Tinaz and Boyac{\i}, Arman and Shalom, Mordechai} } @article {1493, title = {The moderating impact of supply network topology on the effectiveness of risk management}, journal = {International Journal of Production Economics}, volume = {197}, year = {2018}, pages = {13-26}, abstract = {

While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers{\textquoteright} connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience.

}, keywords = {Agent-based modelling, Complex supply networks, Contingent rerouting, Inventory mitigation, Random networks, Scale-free networks, Supply chain risk management}, issn = {0925-5273}, doi = {https://doi.org/10.1016/j.ijpe.2017.12.013}, url = {https://www.sciencedirect.com/science/article/pii/S092552731730419X}, author = {Anna Ledwoch and Yasarcan, Hakan and Alexandra Brintrup} } @article {1450, title = {Queuing systems in a feedback environment: Continuous versus discrete-event simulation}, journal = {Journal of Simulation}, volume = {12}, year = {2018}, pages = {144-161}, abstract = {

AbstractThe discrete stochastic nature of typical queuing problems calls for discrete-event simulation, rather than continuous simulation that would ignore the discrete details that may be important in queuing dynamics. However, continuous simulation can perhaps be suitable for certain large queuing systems that involve feedback interactions and time delays. This study examines this question by a series of simulation experiments with queuing systems. We start with an M/M/2 system with state-dependent arrivals. Since continuous simulation represents the system states by continuous variables, when these variables (number of entities) revolve around small integer values, discrete and continuous simulation approaches exhibit significant differences in their output dynamics. However, when the system scale is increased (many servers, many entities), errors caused by continuity assumption drop significantly and the two simulation approaches yield much closer outputs. Finally, when a delay is introduced before the state of the system influences the arrival rates, the system behaviour becomes oscillatory, involving even sustained oscillations when the delay is discrete. We show that in such settings, continuous simulation can be superior to discrete simulation, since the system exhibits far-from-equilibrium dynamics driven by the endogenous system structure, rather than by discrete stochastic events. Our results have various real-life modelling implications.

}, doi = {10.1080/17477778.2018.1465153}, url = {https://doi.org/10.1080/17477778.2018.1465153}, author = {Yaman Barlas and Onur {\"O}zg{\"u}n} } @article {1442, title = {Robust supply chain network design with multi-products for a company in the food sector}, journal = {Applied Mathematical Modelling}, volume = {60}, year = {2018}, pages = {526-539}, abstract = {

The paper aims to solve a problem faced by a company competing in the snacks market in Turkey. In line with the growth in this market, the company needs to make important decisions over the next few years about the timing and location of a new plant, its initial capacity, the timing and amount of additional capacity to be installed at the new and existing plants, the assignment of demand points to plants and the amount of raw materials to be shipped from suppliers to the plants in each period. The objective is to minimize the total cost of various components. The problem is formulated as a multi-period supply chain network design model with multi products. The resulting mixed-integer linear programming model is solved by the commercial solver CPLEX. This model enables us to carry out all analyses requested by the company in an efficient way. After this deterministic model is solved on the basis of a 9\% annual increase in demand, it is extended to a minimax regret model to deal with uncertainty in demand quantities. The results suggest that opening the new plant in the city of {\.I}zmir is indeed a robust solution that is unaffected in different scenarios that are based on three distinct demand increase rates. Even though the location of the new plant remains unchanged with respect to scenarios, the optimal robust solution differs from the optimal solution of each scenario in terms of the capacity expansion decisions. After all obtained results had been communicated to the company managers and executives, the new plant construction was started in 2016 very close to the city that the mathematical model had determined. The new plant is expected to start operating in 2018.

}, keywords = {Demand uncertainty, Facility location-allocation, Mixed-integer linear programming, Robust optimization, Supply chain network design}, issn = {0307-904X}, doi = {https://doi.org/10.1016/j.apm.2018.03.034}, url = {https://www.sciencedirect.com/science/article/pii/S0307904X18301586}, author = {Necati Aras and {\"U}mit Bilge} } @inbook {1187, title = {Affect measurement: A roadmap through approaches, technologies, and data analysis}, booktitle = {Emotions and Affect in Human Factors and Human-Computer Interaction}, year = {2017}, pages = {255{\textendash}288}, publisher = {Elsevier}, organization = {Elsevier}, author = {Gonzalez-Sanchez, Javier and Mustafa Gokce Baydogan and Chavez-Echeagaray, Maria Elena and Atkinson, Robert K and Burleson, Winslow} } @article {1165, title = {Multi-dimensional clearing functions for aggregate capacity modelling in multi-stage production systems}, journal = {International Journal of Production Research}, volume = {55}, year = {2017}, pages = {4164{\textendash}4179}, author = {Albey, Erin{\c c} and {\"U}mit Bilge and Uzsoy, Reha} } @article {1452, title = {Performance analysis of an aggregation and disaggregation solution procedure to obtain a maintenance plan for a partially observable multi-component system}, journal = {Reliability Engineering \& System Safety}, volume = {167}, year = {2017}, pages = {652-662}, abstract = {

We analyze the performance of an aggregation and disaggregation procedure in giving the optimal maintenance decisions for a multi-component system under partial observations in a finite horizon. The components deteriorate in time and their states are hidden to the decision maker. Nevertheless, it is possible to observe signals about the system status and to replace components in each period. The aim is to find a cost effective replacement plan for the components in a given time horizon. The problem is formulated as a partially observable Markov decision process (POMDP). We aggregate states and actions in order to reduce the problem space and obtain an optimal aggregate policy which we disaggregate by simulating it using dynamic Bayesian networks (DBN). The procedure is statistically compared to an approximate POMDP solver that uses the full state space information. Cases where aggregation performs relatively better are isolated and it is shown that k-out-of-n systems belong to this class.

}, keywords = {Aggregation, Disaggregation, Dynamic Bayesian networks, Maintenance, Multi-component systems, Partially observable Markov decision processes}, issn = {0951-8320}, doi = {https://doi.org/10.1016/j.ress.2017.04.013}, url = {https://www.sciencedirect.com/science/article/pii/S095183201730443X}, author = {Demet {\"O}zg{\"u}r-{\"U}nl{\"u}ak{\i}n and Taner Bilgi{\c c}} } @article {1506, title = {A polynomial-time algorithm for the maximum cardinality cut problem in proper interval graphs}, journal = {Information Processing Letters}, volume = {121}, year = {2017}, pages = {29-33}, abstract = {

It is known that the maximum cardinality cut problem is NP-hard even in chordal graphs. On the positive side, the problem is known to be polynomial time solvable in some subclasses of proper interval graphs which is in turn a subclass of chordal graphs. In this paper, we consider the time complexity of the problem in proper interval graphs, and propose a polynomial-time dynamic programming algorithm.

}, keywords = {Graph algorithms, Maximum cut, Proper interval graph}, issn = {0020-0190}, doi = {https://doi.org/10.1016/j.ipl.2017.01.007}, url = {https://www.sciencedirect.com/science/article/pii/S0020019017300133}, author = {Arman Boyac{\i} and Ekim, Tinaz and Mordechai Shalom} } @conference {1167, title = {A scale-space theory and bag-of-features based time series classification method}, booktitle = {Signal Processing and Communications Applications Conference (SIU), 2017 25th}, year = {2017}, publisher = {IEEE}, organization = {IEEE}, author = {Altay, Tayip and Mustafa Gokce Baydogan} } @article {1510, title = {Graphs of edge-intersecting and non-splitting paths}, journal = {Theoretical Computer Science}, volume = {629}, year = {2016}, pages = {40-50}, abstract = {

The families of Edge Intersection Graphs of Paths in a tree (resp. in a grid) EPT (resp. EPG) are well studied graph classes. Recently we introduced the class of graphs of Edge-Intersecting and Non-Splitting Paths in a Tree (ENPT) [5]. In this model, two vertices are adjacent if they represent two intersecting paths of a tree whose union is also a path. In this study we generalize this graph class by allowing the host graph to be an arbitrary graph. These are the graphs of Edge-Intersecting and Non-Splitting Paths ENP. Although the Edge Intersection Graphs of Paths in an arbitrary graph includes all graphs, we show that this is not the case for ENP. We also show that the class ENP coincides with the family of graphs of Edge-Intersecting and Non-Splitting Paths in a Grid (ENPG). Following similar studies for EPG graph class, we study the implications of restricting the number of bends of the individual paths in the grid. We show that such a restriction restricts the graph class. Specifically, by restricting the number of bends one gets an infinite sequence of classes such that every class is properly included in the next one.

}, keywords = {EPG graphs, EPT graphs, Intersection graphs}, issn = {0304-3975}, doi = {https://doi.org/10.1016/j.tcs.2015.10.004}, url = {https://www.sciencedirect.com/science/article/pii/S0304397515008750}, author = {Arman Boyac{\i} and Ekim, Tinaz and Mordechai Shalom and Shmuel Zaks} } @article {1170, title = {Graphs of edge-intersecting non-splitting paths in a tree: Representations of holes{\textemdash}Part I}, journal = {Discrete Applied Mathematics}, volume = {215}, year = {2016}, pages = {47{\textendash}60}, author = {Boyac{\i}, Arman and Ekim, Tinaz and Shalom, Mordechai and Zaks, Shmuel} } @article {1174, title = {Innovation race under revenue and technology uncertainty of heterogeneous firms where the winner does not take all}, journal = {IIE Transactions}, volume = {48}, year = {2016}, pages = {527{\textendash}540}, author = {Taner Bilgi{\c c} and Refik G{\"u}ll{\"u}} } @proceedings {1190, title = {Modeling the Long Term Dynamics of Obesity as a Societal Epidemic}, journal = {34th International Conference of the System Dynamics Society}, year = {2016}, address = {Delft, the Netherlands}, author = {Akta{\c s}, Gizem and Nefel Tellio{\u g}lu and Yaman Barlas and Hakan Ya{\c s}arcan} } @article {1169, title = {PSO-based and SA-based metaheuristics for bilinear programming problems: an application to the pooling problem}, journal = {Journal of Heuristics}, volume = {22}, year = {2016}, pages = {147{\textendash}179}, author = {G{\"o}kalp Erbeyo{\u g}lu and {\"U}mit Bilge} } @article {1168, title = {Sensitivity analysis for models with multiple behavior modes: a method based on behavior pattern measures}, journal = {System Dynamics Review}, volume = {32}, year = {2016}, pages = {332{\textendash}362}, author = {Hekimo{\u g}lu, Mustafa and Yaman Barlas} } @article {1063, title = {Time series representation and similarity based on local autopatterns}, journal = {Data Mining and Knowledge Discovery}, volume = {30}, year = {2016}, month = {03/2016}, pages = {476{\textendash}509}, abstract = {

Time series data mining has received much greater interest along with the increase in temporal data sets from different domains such as medicine, finance, multimedia, etc. Representations are important to reduce dimensionality and generate useful similarity measures. High-level representations such as Fourier transforms, wavelets, piecewise polynomial models, etc., were considered previously. Recently, autoregressive kernels were introduced to reflect the similarity of the time series. We introduce a novel approach to model the dependency structure in time series that generalizes the concept of autoregression to local autopatterns. Our approach generates a pattern-based representation along with a similarity measure called learned pattern similarity (LPS). A tree-based ensemble-learning strategy that is fast and insensitive to parameter settings is the basis for the approach. Then, a robust similarity measure based on the learned patterns is presented. This unsupervised approach to represent and measure the similarity between time series generally applies to a number of data mining tasks (e.g., clustering, anomaly detection, classification). Furthermore, an embedded learning of the representation avoids pre-defined features and an extraction step which is common in some feature-based approaches. The method generalizes in a straightforward manner to multivariate time series. The effectiveness of LPS is evaluated on time series classification problems from various domains. We compare LPS to eleven well-known similarity measures. Our experimental results show that LPS provides fast and competitive results on benchmark datasets from several domains. Furthermore, LPS provides a research direction and template approach that breaks from the linear dependency models to potentially foster other promising nonlinear approaches.

}, issn = {1573-756X}, author = {Mustafa Gokce Baydogan and Runger, George} } @article {guler2015joint, title = {Joint pricing and inventory control for additive demand models with reference effects}, journal = {Annals of Operations Research}, volume = {226}, number = {1}, year = {2015}, pages = {255{\textendash}276}, publisher = {Springer US}, author = {G{\"u}ler, M G{\"u}ray and Taner Bilgi{\c c} and Refik G{\"u}ll{\"u}} } @article {657, title = {Learning a symbolic representation for multivariate time series classification}, journal = {Data Mining and Knowledge Discovery}, volume = {29}, year = {2015}, month = {03/2015}, pages = {400-422}, publisher = {Springer US}, abstract = {

Multivariate time series (MTS) classification has gained importance with the increase in the number of temporal datasets in different domains (such as medicine, finance, multimedia, etc.). Similarity-based approaches, such as nearest-neighbor classifiers, are often used for univariate time series, but MTS are characterized not only by individual attributes, but also by their relationships. Here we provide a classifier based on a new symbolic representation for MTS (denoted as SMTS) with several important elements. SMTS considers all attributes of MTS simultaneously, rather than separately, to extract information contained in the relationships. Symbols are learned from a supervised algorithm that does not require pre-defined intervals, nor features. An elementary representation is used that consists of the time index, and the values (and first differences for numerical attributes) of the individual time series as columns. That is, there is essentially no feature extraction (aside from first differences) and the local series values are fused to time position through the time index. The initial representation of raw data is quite simple conceptually and operationally. Still, a tree-based ensemble can detect interactions in the space of the time index and time values and this is exploited to generate a high-dimensional codebook from the terminal nodes of the trees. Because the time index is included as an attribute, each MTS is learned to be segmented by time, or by the value of one of its attributes. The codebook is processed with a second ensemble where now implicit feature selection is exploited to handle the high-dimensional input. The constituent properties produce a distinctly different algorithm. Moreover, MTS with nominal and missing values are handled efficiently with tree learners. Experiments demonstrate the effectiveness of the proposed approach in terms of accuracy and computation times in a large collection multivariate (and univariate) datasets.

}, keywords = {codebook, Decision trees, supervised learning}, issn = {1384-5810}, doi = {10.1007/s10618-014-0349-y}, url = {http://dx.doi.org/10.1007/s10618-014-0349-y}, author = {Mustafa Gokce Baydogan and Runger, George} } @article {786, title = {Mathematical Programming-Based Sales and Operations Planning at Vestel Electronics}, journal = {Interfaces}, volume = {45}, year = {2015}, pages = {325-340}, doi = {http://dx.doi.org/10.1287/inte.2015.0793}, author = {Z C Ta{\c s}k{\i}n and Semra A{\u g}ral{\i} and Ali Tamer {\"U}nal and Vahdet Belada and Filiz G{\"o}kten-Y{\i}lmaz} } @article {908, title = {Metabolic connectivity as index of verbal working memory}, journal = {Journal of cerebral blood flow and metabolism}, volume = {35}, year = {2015}, month = {July}, pages = {1122{\textemdash}1126}, issn = {0271-678X}, doi = {10.1038/jcbfm.2015.40}, url = {http://dx.doi.org/10.1038/jcbfm.2015.40}, author = {Zou, Na and Chetelat, Gael and Mustafa Gokce Baydogan and Li, Jing and Fischer, Florian U and Titov, Dmitry and Dukart, Juergen and Fellgiebel, Andreas and Schreckenberger, Mathias and Yakushev, Igor} } @article {941, title = {A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems}, journal = {Technometrics}, volume = {57}, year = {2015}, pages = {362-373}, doi = {10.1080/00401706.2015.1044117}, url = {http://dx.doi.org/10.1080/00401706.2015.1044117}, author = {Na Zou and Yun Zhu and Ji Zhu and Mustafa Gokce Baydogan and Wei Wang and Jing Li} } @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 {1459, title = {On Censored Bivariate Random Variables: Copula, Characterization, and Estimation}, journal = {Communications in Statistics - Simulation and Computation}, volume = {43}, year = {2014}, pages = {2173-2185}, abstract = {

{ Let (X, Y) be a bivariate random vector with joint distribution function FX, Y(x, y) = C(F(x), G(y)), where C is a copula and F and G are marginal distributions of X and Y, respectively. Suppose that (Xi, Yi)

}, doi = {10.1080/03610918.2012.748911}, url = {https://doi.org/10.1080/03610918.2012.748911}, author = {K{\"o}n{\"u}l Bayramo{\u g}lu Kavlak and Ismihan Bayramoglu (bairamov)} } @article {albey2014exploratory, title = {An exploratory study of disaggregated clearing functions for production systems with multiple products}, journal = {International Journal of Production Research}, number = {ahead-of-print}, year = {2014}, pages = {1{\textendash}22}, publisher = {Taylor \& Francis}, author = {Albey, Erin{\c c} and {\"U}mit Bilge and Uzsoy, Reha} } @article {guler2014joint, title = {Joint inventory and pricing decisions when customers are delay sensitive}, journal = {International Journal of Production Economics}, volume = {157}, year = {2014}, pages = {302{\textendash}312}, publisher = {Elsevier}, author = {G{\"u}ler, M G{\"u}ray and Taner Bilgi{\c c} and Refik G{\"u}ll{\"u}} } @article {GBG2014a, title = {Joint inventory and pricing decisions with reference effects}, journal = {IIE Transactions}, volume = {46}, number = {4}, year = {2014}, pages = {330{\textendash}343}, publisher = {Taylor \& Francis Group}, author = {G{\"u}ler, M G{\"u}ray and Taner Bilgi{\c c} and Refi̇k G{\"u}ll{\"u}} } @article {._zba__2014, title = {Modeling and Simulation of the Endogenous Dynamics of Housing Market Cycles}, journal = {Journal of Artificial Societies and Social Simulation}, volume = {17}, number = {1}, year = {2014}, pages = {19}, abstract = {

The purpose of this study is to model and analyze by simulation the dynamics of endogenously created oscillations in real estate (housing) prices. A system dynamics simulation model is built to understand some of the structural sources of cycles in the key housing market variables, from the perspective of construction companies. The model focuses on the economic balance dynamics between supply and demand. Because of the unavoidable delays in the perception of the real estate market conditions and construction of new buildings, prices and related market variables exhibit strong oscillations. Two policies are tested to reduce the oscillations: decreasing the construction time, and taking into account the houses under construction in starting new projects. Both policies yield significantly reduced oscillations, more stable behaviors.

}, keywords = {Housing Cycles, Price Oscillations, Real Estate Modeling, Socio-Economic Simulation, System Dynamics}, issn = {1460-7425}, url = {http://jasss.soc.surrey.ac.uk/17/1/19.html}, author = {Birnur {\"O}zba{\c s} and Onur {\"O}zg{\"u}n and Yaman Barlas} } @article {bacsdere2014operational, title = {Operational aircraft maintenance routing problem with remaining time consideration}, journal = {European Journal of Operational Research}, volume = {235}, number = {1}, year = {2014}, pages = {315{\textendash}328}, publisher = {Elsevier}, author = {Ba{\c s}dere, Mehmet and {\"U}mit Bilge} } @article {ozgur2014replacement, title = {Replacement policies for a complex system with unobservable components using dynamic Bayesian networks}, journal = {International Journal of Computational Intelligence Systems}, volume = {7}, number = {sup1}, year = {2014}, pages = {68{\textendash}83}, publisher = {Taylor \& Francis}, author = {{\"O}zg{\"u}r-{\"U}nl{\"u}akin, Demet and Taner Bilgi{\c c}} } @article {35, title = {SMT: Sparse multivariate tree}, journal = {Statistical Analysis and Data Mining}, volume = {7}, year = {2014}, month = {02/2014}, pages = {53-69}, abstract = {

A multivariate decision tree attempts to improve upon the single variable split in a traditional tree. With the increase in datasets with many features and a small number of labeled instances in a variety of domains (bioinformatics, text mining, etc.), a traditional tree-based approach with a greedy variable selection at a node may omit important information. Therefore, the recursive partitioning idea of a simple decision tree combined with the intrinsic feature selection of L1 regularized logistic regression (LR) at each node is a natural choice for a multivariate tree model that is simple, but broadly applicable. This natural solution leads to the sparse multivariate tree (SMT) considered here. SMT can naturally handle non-time-series data and is extended to handle time-series classification problems with the power of extracting interpretable temporal patterns (e.g., means, slopes, and deviations). Binary L1 regularized LR models are used here for binary classification problems. However, SMT may be extended to solve multiclass problems with multinomial LR models. The accuracy and computational efficiency of SMT is compared to a large number of competitors on time series and non-time-series data.

}, keywords = {decision tree, feature extraction, fused Lasso, Lasso, time series classification}, issn = {1932-1872}, doi = {10.1002/sam.11208}, url = {http://dx.doi.org/10.1002/sam.11208}, author = {Houtao Deng and Mustafa Gokce Baydogan and George Runger} } @conference {39, title = {Affect Recognition in Learning Scenarios: Matching Facial-and BCI-Based Values}, booktitle = {13th IEEE International Conference on Advanced Learning Technologies (ICALT{\textquoteright}13)}, year = {2013}, publisher = {IEEE}, organization = {IEEE}, author = {Javier Gonzalez-Sanchez and Maria Elena Chavez-Echeagaray and Lijia Lin and Mustafa Gokce Baydogan and Robert Christopherson and David Gibson and Robert Atkinson and Winslow Burleson} } @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 {BodurEtAl2013, title = {Decomposition Algorithms for Solving the Minimum Weight Maximal Matching Problem}, journal = {Networks}, volume = {62}, number = {4}, year = {2013}, pages = {273{\textendash}287}, doi = {http://dx.doi.org/10.1002/net.21516}, author = {Merve Bodur and Ekim, Tinaz and Z C Ta{\c s}k{\i}n} } @article {1464, title = {From the Huang Kotz FGM distribution to Baker s bivariate distribution}, journal = {Journal of Multivariate Analysis}, volume = {113}, year = {2013}, month = {Jan}, pages = {106{\textendash}115}, issn = {0047259X}, author = {BAYRAMO{\u G}LU, {\.I}SM{\.I}HAN and K{\"o}n{\"u}l Bayramo{\u g}lu Kavlak} } @article {boyaci2013graphs, title = {Graphs of Edge-Intersecting Non-splitting Paths in a Tree: Towards Hole Representations}, year = {2013}, author = {Boyac{\i}, Arman and Ekim, Tinaz and Shalom, Mordechai and Zaks, Shmuel} } @article {bacsdere2013leader, title = {A leader-follower game for the point coverage problem in wireless sensor networks}, journal = {European Journal of Industrial Engineering}, volume = {7}, number = {5}, year = {2013}, pages = {635{\textendash}656}, publisher = {Inderscience Publishers}, author = {Ba{\c s}dere, Mehmet and Necati Aras and I. Kuban Altinel and Af{\c s}ar, Sezin} } @article {bilge2013mathematical, title = {Mathematical models for FMS loading and part type selection with flexible process plans}, journal = {European Journal of Industrial Engineering}, year = {2013}, publisher = {Inderscience}, author = {{\"U}mit Bilge and Albey, Erin{\c c} and Be{\c s}ikci, Umut and Erbatur, Kemalettin and Aslan, Ay{\c s}e Nur} } @inbook {bilgicc2013membership, title = {The Membership Function and Its Measurement}, booktitle = {On Fuzziness}, year = {2013}, pages = {47{\textendash}53}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, author = {Taner Bilgi{\c c}}, editor = {Seising R, Trillas E, Moraga C, Termini S} } @proceedings {nisa2, title = {Modeling the Dynamics of Methylmercury Biomagnification}, journal = {31st International Conference of the System Dynamics Society}, year = {2013}, author = {Bacaks{\i}zlar, N. G. and Nisa {\"O}nsel} } @article {bacsouglu2013optimally, title = {Optimally stratified importance sampling for portfolio risk with multiple loss thresholds}, journal = {Optimization}, volume = {62}, number = {11}, year = {2013}, pages = {1451{\textendash}1471}, publisher = {Taylor \& Francis}, author = {Ba{\c s}o{\u g}lu, {\.I}smail and Wolfgang H{\"o}rmann and Sak, Halis} } @article {bonomo2013perfectness, title = {Perfectness of clustered graphs}, journal = {Discrete Optimization}, volume = {10}, number = {4}, year = {2013}, pages = {296{\textendash}303}, publisher = {Elsevier}, author = {Bonomo, Flavia and Cornaz, Denis and Ekim, Tinaz and Ries, Bernard} } @article {becsikci2013resource, title = {Resource dedication problem in a multi-project environment}, journal = {Flexible Services and Manufacturing Journal}, volume = {25}, number = {1-2}, year = {2013}, pages = {206{\textendash}229}, publisher = {Springer}, author = {Be{\c s}ikci, Umut and {\"U}mit Bilge and Ulusoy, G{\"u}nd{\"u}z} } @article {botts2013transformed, title = {Transformed density rejection with inflection points}, journal = {Statistics and Computing}, volume = {23}, number = {2}, year = {2013}, pages = {251{\textendash}260}, publisher = {Springer US}, author = {Botts, Carsten and Wolfgang H{\"o}rmann and Leydold, Josef} } @article {fas2013two, title = {A two-period dynamic game for a substitutable product inventory control problem}, journal = {International Journal of Inventory Research}, volume = {2}, number = {1-2}, year = {2013}, pages = {108{\textendash}126}, publisher = {Inderscience Publishers Ltd}, author = {Fas, Genco and Taner Bilgi{\c c}} } @proceedings {ozgun, title = {Effects of Delay, Nonlinearity and Feedback on the Overall Complexity of a Stock Management Game}, journal = {30th International Conference of the System Dynamics Society}, year = {2012}, author = {Onur {\"O}zg{\"u}n and Yaman Barlas} } @article {kocsucuouglu2012material, title = {Material handling considerations in the FMS loading problem with full routing flexibility}, journal = {International Journal of Production Research}, volume = {50}, number = {22}, year = {2012}, pages = {6530{\textendash}6552}, publisher = {Taylor \& Francis}, author = {Ko{\c s}ucuo{\u g}lu, Deniz and {\"U}mit Bilge} } @proceedings {ozgun, title = {Systemic Complexity of a Growth Management Game: Comparative Analysis of Decision Heuristics and Experimental Results}, journal = {30th International Conference of the System Dynamics Society}, year = {2012}, author = {Onur {\"O}zg{\"u}n and Yaman Barlas} } @article {doyen2012two, title = {A two-echelon stochastic facility location model for humanitarian relief logistics}, journal = {Optimization Letters}, volume = {6}, number = {6}, year = {2012}, pages = {1123{\textendash}1145}, publisher = {Springer-Verlag}, author = {D{\"o}yen, Alper and Necati Aras and Barbaroso{\u g}lu, G{\"u}lay} } @article {Yucel2011, title = {Automated parameter specification in dynamic feedback models based on behavior pattern features}, journal = {System Dynamics Review}, volume = {27}, number = {2}, year = {2011}, pages = {195{\textendash}215}, publisher = {Wiley Online Library}, keywords = {Calibration}, doi = {10.1002/sdr}, url = {http://onlinelibrary.wiley.com/doi/10.1002/sdr.457/full}, author = {G{\"o}nen{\c c} Y{\"u}cel and Yaman Barlas} } @article {barlas2011demand, title = {Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains}, journal = {Journal of the Operational Research Society}, volume = {62}, number = {3}, year = {2011}, pages = {458{\textendash}473}, publisher = {Nature Publishing Group}, author = {Yaman Barlas and Baris Gunduz} } @article {becsikcci2011different, title = {Different resource management policies in multi-mode resource constrained multi-project scheduling}, year = {2011}, publisher = {Institute of Electrical and Electronics Engineers, Inc.}, author = {Be{\c s}ikci, Umut and {\"U}mit Bilge and Ulusoy, G{\"u}nd{\"u}z} } @article {albey2011hierarchical, title = {A hierarchical approach to FMS planning and control with simulation-based capacity anticipation}, journal = {International Journal of Production Research}, volume = {49}, number = {11}, year = {2011}, pages = {3319{\textendash}3342}, publisher = {Taylor \& Francis}, author = {Albey, Erin{\c c} and {\"U}mit Bilge} } @proceedings {nisa3, title = {Modeling the Dynamics of Academic Publications and Citations}, journal = {29th International Conference of the System Dynamics Society}, year = {2011}, author = {Nisa {\"O}nsel and Yaman Barlas} } @proceedings {seker, title = {Modeling the Dynamics of Thyroid Hormones and Related Disorders}, journal = {29th International Conference of the System Dynamics Society}, year = {2011}, author = {Oylum {\c S}eker and Yaman Barlas and Faruk Alag{\"o}l} } @article {aras2010designing, title = {Designing the reverse logistics network}, journal = {Closed-loop supply chains: new developments to improve the sustainability of business practices. Auerbach Publications}, year = {2010}, pages = {67{\textendash}97}, author = {Necati Aras and Boyaci, Tamer and Verter, Vedat and Ferguson, M and Souza, GC} } @article {Yucel2010, title = {Dynamics of the North{\textendash}South welfare gap and global sustainability}, journal = {Technological Forecasting and Social Change}, volume = {77}, number = {4}, year = {2010}, pages = {594{\textendash}614}, publisher = {Elsevier Inc.}, keywords = {north, south welfare gap}, issn = {00401625}, doi = {10.1016/j.techfore.2009.11.004}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0040162509001887}, author = {G{\"o}nen{\c c} Y{\"u}cel and Yaman Barlas} } @article {erol2010exploring, title = {Exploring reverse supply chain management practices in Turkey}, journal = {Supply Chain Management: An International Journal}, volume = {15}, number = {1}, year = {2010}, pages = {43{\textendash}54}, publisher = {Emerald Group Publishing Limited}, author = {Erol, Ismail and Velioglu, Meltem Nurtanis and Serifoglu, Funda Sivrikaya and B{\"u}y{\"u}k{\"o}zkan, G{\"u}l{\c c}in and Necati Aras and Cakar, Nigar Demircan and Aybek Korugan} } @inbook {bilgicc2010operational, title = {Operational Research Society of Turkey}, booktitle = {Wiley Encyclopedia of Operations Research and Management Science}, year = {2010}, publisher = {John Wiley \& Sons, Inc.}, organization = {John Wiley \& Sons, Inc.}, author = {Taner Bilgi{\c c} and Refik G{\"u}ll{\"u}} } @article {guler2009coordinating, title = {On coordinating an assembly system under random yield and random demand}, journal = {European Journal of Operational Research}, volume = {196}, number = {1}, year = {2009}, pages = {342{\textendash}350}, publisher = {Elsevier}, author = {G{\"u}ler, M G{\"u}ray and Taner Bilgi{\c c}} } @article {1471, title = {Performance analysis of a hybrid system under quality impact of returns}, journal = {Computers \& Industrial Engineering}, volume = {56}, year = {2009}, month = {Mar}, pages = {507{\textendash}520}, issn = {03608352}, author = {Behret, H{\"u}lya and Aybek Korugan} } @article {34, title = {Toward Development of Adaptive Service-Based Software Systems}, journal = {IEEE Transactions on Services Computing}, volume = {2}, year = {2009}, pages = {247-260}, abstract = {

The rapid adoption of service-oriented architecture (SOA) in many large-scale distributed applications requires the development of adaptive service-based software systems (ASBS) with the capability of monitoring the changing system status, analyzing, and controlling tradeoffs among various quality-of-service (QoS) aspects, and adapting service configurations to satisfy multiple QoS requirements simultaneously. In this paper, our results toward the development of adaptive service-based software systems are presented. The formulation of activity-state-QoS (ASQ) models and how to use the data from controlled experiments to establish ASQ models for capturing the cause-effect dynamics among service activities, system resource states, and QoS in service-based systems are presented. Then, QoS monitoring modules based on ASQ models and SOA-compliant simulation models are developed to support the validation of the ASBS design. The main idea for developing QoS adaptation modules based on ASQ models is discussed. An experiment based on a voice communication service is used to illustrate our results.

}, keywords = {Design concepts, distributed/Internet-based software engineering tools and techniques, methodologies, modeling methodologies, quality of services, services systems}, issn = {1939-1374}, doi = {http://doi.ieeecomputersociety.org/10.1109/TSC.2009.17}, author = {Stephen S. Yau and Nong Ye and Hessam S. Sarjoughian and Dazhi Huang and Auttawut Roontiva and Mustafa Gokce Baydogan and Mohammed A. 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