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Publication Metadata only A bi-objective model for design and analysis of sustainable intermodal transportation systems: A case study of Turkey(Taylor & Francis Ltd, 2019) Reşat, Hamdi Giray; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a mixed-integer linear optimisation model to analyse the intermodal transportation systems in the Turkish transportation industry. The solution approach includes mathematical modelling, data analysis from real-life cases and solving the resulting mathematical programming problem to minimise total transportation cost and carbon dioxide emissions by using two different exact solution methods in order to find the optimal solutions. The novel approach of this paper generates Pareto solutions quickly and allows the decision makers to identify sustainable solutions by using a newly developed solution methodology for bi-objective mixed-integer linear problems in real-life cases.Publication Metadata only A discrete-continuous optimization approach for the design and operation of synchromodal transportation networks(Elsevier, 2019) Reşat, Hamdi Giray; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a multi-objective mixed-integer programming problem for integrating specific characteristics of synchromodal transportation. The problem includes different objective functions including total transportation cost, travel time and CO2 emissions while optimizing the proposed network structure. Traffic congestion, time-dependent vehicle speeds and vehicle filling ratios are considered and computational results for different illustrative cases are presented with real data from the Marmara Region of Turkey. The defined non-linear model is converted into linear form and solved by using a customized implementation of the e-constraint method. Then, the sensitivity analysis of proposed mathematical models with pre-processing constraints is summarized for decision makers.Publication Metadata only A hyper-heuristic approach to sequencing by hybridization of DNA sequences(Springer, 2013) Blazewicz, Jacek; Burke, Edmund K.; Kendall, Graham; Mruczkiewicz, Wojciech; Swiercz, Aleksandra; Department of Industrial Engineering; Oğuz, Ceyda; Faculty Member; Department of Industrial Engineering; College of Engineering; 6033In this paper we investigate the use of hyper-heuristic methodologies for predicting DNA sequences. In particular, we utilize Sequencing by Hybridization. We believe that this is the first time that hyper-heuristics have been investigated in this domain. A hyper-heuristic is provided with a set of low-level heuristics and the aim is to decide which heuristic to call at each decision point. We investigate three types of hyper-heuristics. Two of these (simulated annealing and tabu search) draw their inspiration from meta-heuristics. The choice function hyper-heuristic draws its inspiration from reinforcement learning. We utilize two independent sets of low-level heuristics. The first set is based on a previous tabu search method, with the second set being a significant extension to this basic set, including utilizing a different representation and introducing the definition of clusters. The datasets we use comprises two randomly generated datasets and also a publicly available biological dataset. In total, we carried out experiments using 70 different combinations of heuristics, using the three datasets mentioned above and investigating six different hyper-heuristic algorithms. Our results demonstrate the effectiveness of a hyper-heuristic approach to this problem domain. It is necessary to provide a good set of low-level heuristics, which are able to both intensify and diversify the search but this approach has demonstrated very encouraging results on this extremely difficult and important problem domain.Publication Metadata only A Markov modulated Poisson model for software reliability(Elsevier, 2013) Landon, Joshua; Soyer, Refik; Department of Industrial Engineering; Özekici, Süleyman; Faculty Member; Department of Industrial Engineering; College of Engineering; 32631In this paper, we consider a latent Markov process governing the intensity rate of a Poisson process model for software failures. The latent process enables us to infer performance of the debugging operations over time and allows us to deal with the imperfect debugging scenario. We develop the Bayesian inference for the model and also introduce a method to infer the unknown dimension of the Markov process. We illustrate the implementation of our model and the Bayesian approach by using actual software failure data.Publication Metadata only A matheuristic for the generalized order acceptance and scheduling problem(Elsevier, 2022) N/A; Department of Industrial Engineering; Tarhan, İstenç; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 6033In make-to-order production systems, manufacturer can have limited capacity and due to the order de-livery time requirements, it may not be possible to accept all orders. This leads to the order acceptance and scheduling problem with release times and sequence dependent setup times that determines which orders to accept and how to schedule them simultaneously to maximize the revenue (GOAS). The aim of this study is to develop an effective and efficient solution methodology for the GOAS problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model, and a matheuristic algorithm that consists of a time-bucket based mixed integer linear programming model, a variable neighborhood search algorithm and a tabu search algorithm. Computational results show that the proposed matheuristic outperforms both the proposed exact models and previous state-of-the-art al-gorithms developed for the GOAS problem. The boundary of optimally solved instance size is pushed further and near optimal solutions are obtained in reasonable time for instances falling beyond this boundary.Publication Metadata only A mixed-integer programming approach to multi-class data classification problem(Elsevier Science Bv, 2006) Department of Industrial Engineering; Department of Industrial Engineering; Yüksektepe, Fadime Üney; Türkay, Metin; Researcher; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 108243; 24956This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer programming model is developed for representing existence of hyper-boxes and their boundaries. In addition, the relationships among the discrete decisions in the model are represented using propositional logic and then converted to their equivalent integer constraints using Boolean algebra. The proposed approach for multi-class data classification is illustrated on an example problem. The efficiency of the proposed method is tested on the well-known IRIS data set. The computational results on the illustrative example and the IRIS data set show that the proposed method is accurate and efficient on multi-class data classification problems.Publication Metadata only A model for estimating the carbon footprint of maritime transportation of liquefied natural gas under uncertainty(2021) Aseel, Saleh; Al-Yafei, Hussein; Küçükvar, Murat; Onat, Nuri C.; Kazançoğlu, Yiğit; Al-Sulaiti, Ahmed; Al-Hajri, Abdulla; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956The demand for Liquefied Natural Gas (LNG) in the global markets has changed significantly. As a result, industries have been forced to consider investing significantly in supply chains to achieve an efficient distribution of LNG for cost efficiency and carbon footprint reduction. To minimize the contribution of LNG maritime transportation to global climate change, there is a need to quantify the carbon footprints systematically. In this research, we developed a novel and practical model for estimating the carbon footprint for LNG maritime transport. Using the MATLAB program, an uncertainty-based carbon footprint accounting framework is created. The Monte Carlo simulation model is built to conduct a carbon footprint analysis while the main input parameters were changed within a reliable range. Later, a multivariate sensitivity analysis is performed using the Risk Solver software to estimate the most significant parameters on the net carbon footprints. The sensitivity analysis results showed that that steam process day and steaming fuel consumption are found to be the most sensitive parameters for the overall carbon footprint for both Laden and Ballast trips. Furthermore, it was found that the Q-Max vessel produces more carbon emissions when compared to the Q-Flex, although both are traveling the same distance and are using the same fuel type. The type of fuel is also significantly affecting the emission values due to the relevant carbon content in the fuel. Like the case of the two conventional vessels, the one that is running with the only LNG is found to have fewer emissions when compared to the one run with dual-mode.Publication Open Access A multi-criteria decision analysis to include environmental, social, and cultural issues in the sustainable aggregate production plans(Elsevier, 2019) Department of Industrial Engineering; Türkay, Metin; Rasmi, Seyyed Amir Babak; Kazan, Cem; Faculty Member; PhD Student; Department of Industrial Engineering; Graduate School of Sciences and Engineering; 24956; N/A; N/AAggregate production planning (APP) that is an important concept of supply chain management (SCM), is one of the tools to determine production rates, inventory levels, and workforce requirements for fulfilling customer demands in a multi-period setting. Traditional APP models employ a single objective function to optimize monetary issues only. In this paper, we present a multi-objective APP model to analyze economic, social, environmental, and cultural pillars inclusively; moreover, each pillar includes several sub-pillars in the model. The resulting model includes an accurate representation of the problem with binary and continuous variables under sustainability considerations. We illustrate the effectiveness of the model in an appliance manufacturer and solve the problem using an exact solution method for multi-objective mixed-integer linear programs (MOMILP). We find a large number of the non-dominated (ND) points in the objective function space and analyze their trade-offs systematically. We show how this framework supports multiple criteria decision making process in the APP problems in the presence of sustainability considerations. Our approach provides a comprehensive analysis of the ND points of sustainable APP (SAPP) problems, and hence, the trade-offs of objective functions are insightful to the decision makers.Publication Metadata only A multicenter international study to evaluate different aspects of relationship between MS and pregnancy(Sage, 2019) Zakaria, M.; Alroughani, R.; Moghadasi, A. N.; Terzi, M.; Sen, S.; Koseoglu, M.; Efendi, H.; Soysal, A.; Gozubatik-Celik, G.; Ozturk, M.; Sahraian, M.; Akinci, Y.; Kaya, Z. E.; Saip, S.; Siva, A.; N/A; Department of Industrial Engineering; Altıntaş, Ayşe; Gönen, Mehmet; Faculty Member; Faculty Member; Department of Industrial Engineering; School of Medicine; College of Engineering; 11611; 237468N/APublication Metadata only A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers(Oxford University Press (OUP), 2020) N/A; N/A; Department of Industrial Engineering; Rahimi, Arezou; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction. Results: We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature.