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Publication Metadata only A bi-criteria optimization model to analyze the impacts of electric vehicles on costs and emissions(Elsevier, 2017) N/A; N/A; Department of Industrial Engineering; Kabatepe, Bora; Türkay, Metin; Master Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956Electric vehicles (EV) are emerging as a mobility solution to reduce emissions in the transportation sector. The studies environmental impact analysis of EVs in the literature are based on the average energy mix or pre-defined generation scenarios and construct policy recommendations with a cost minimization objective. However, the environmental performance of EVs depends on the source of the marginal electricity provided to the grid and single objective models do not provide a thorough analysis on the economic and environmental impacts of EVs. In this paper, these gaps are addressed by a four step methodology that analyzes the effects of EVs under different charging and market penetration scenarios. The methodology includes a bi-criteria optimization model representing the electricity market operations. The results from a real-life case analysis show that EVs decrease costs and emissions significantly compared to conventional vehicles.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 binarization strategy for modelling mixed data in multigroup classification(Institute of Electrical and Electronics Engineers (IEEE), 2013) Masmoudi, Youssef; Chabchoub, Habib; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes with a wide range to use a small number of binary attributes. Once the data set is pre-processed, we use the Support Vector Machine (LibSVM) for classification. The proposed method was tested on datasets from the literature. We demonstrate the improved accuracy and efficiency of presented binarization strategy for modelling mixed and complex data in comparison to the classification of the original dataset, nominal dataset and binary dataset.Publication Metadata only A CLOCK-binding small molecule disrupts the interaction between CLOCK and BMAL1 and enhances circadian rhythm amplitude(Elsevier, 2020) Akyel, Yasemin Kübra; Yılmaz, Fatma; Öztürk, Nuri; Öztürk, Narin; Okyar, Alper; N/A; N/A; Department of Chemical and Biological Engineering; N/A; Department of Molecular Biology and Genetics; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Doruk, Yağmur Umay; Yarparvar, Darya; Gül, Şeref; Taşkın, Ali Cihan; Barış, İbrahim; Türkay, Metin; Kavaklı, İbrahim Halil; Master Student; PhD Student; Researcher; Other; Teaching Faculty; Faculty Member; Faculty Member; Department of Molecular Biology and Genetics; Department of Industrial Engineering; Department of Chemical and Biological Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; College of Sciences; College of Engineering; College of Engineering; N/A; N/A; N/A; 291296; 111629; 24956; 40319Proper function of many physiological processes requires a robust circadian clock. Disruptions of the circadian clock can result in metabolic diseases, mood disorders, and accelerated aging. Therefore, identifying small molecules that specifically modulate regulatory core clock proteins may potentially enable better management of these disorders. In this study, we applied a structure-based molecular-docking approach to find small molecules that specifically bind to the core circadian regulator, the transcription factor circadian locomotor output cycles kaput (CLOCK). We identified 100 candidate molecules by virtual screening of ?2 million small molecules for those predicted to bind closely to the interface in CLOCK that interacts with its transcriptional co-regulator, Brain and muscle Arnt-like protein-1 (BMAL1). Using a mammalian two-hybrid system, real-time monitoring of circadian rhythm in U2OS cells, and various biochemical assays, we tested these compounds experimentally and found one, named CLK8, that specifically bound to and interfered with CLOCK activity. We show that CLK8 disrupts the interaction between CLOCK and BMAL1 and interferes with nuclear translocation of CLOCK both in vivo and in vitro. Results from further experiments indicated that CLK8 enhances the amplitude of the cellular circadian rhythm by stabilizing the negative arm of the transcription/translation feedback loop without affecting period length. Our results reveal CLK8 as a tool for further studies of CLOCK's role in circadian rhythm amplitude regulation and as a potential candidate for therapeutic development to manage disorders associated with dampened circadian rhythms.Publication Metadata only A constant-factor approximation algorithm for multi-vehicle collection for processing problem(Springer Heidelberg, 2013) Gel, Esma S.; N/A; Department of Industrial Engineering; Department of Industrial Engineering; Yücel, Eda; Salman, Fatma Sibel; Örmeci, Lerzan; PhD Student; Faculty Member; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 235501; 178838; 32863We define the multiple-vehicle collection for processing problem (mCfPP) as a vehicle routing and scheduling problem in which items that accumulate at customer sites over time should be transferred by a series of tours to a processing facility. We show that this problem with the makespan objective (mCfPP()) is NP-hard using an approximation preserving reduction from a two-stage, hybrid flowshop scheduling problem. We develop a polynomial-time, constant-factor approximation algorithm to solve mCfPP(). The problem with a single site is analyzed as a special case with two purposes. First, we identify the minimum number of vehicles required to achieve a lower bound on the makespan, and second, we characterize the optimal makespan when a single vehicle is utilized.Publication Metadata only A coordinated production and shipment model in a supply chain(Elsevier Science Bv, 2013) N/A; Department of Industrial Engineering; N/A; Department of Industrial Engineering; Kaya, Onur; Kubalı, Deniz; Örmeci, Lerzan; Faculty Member; Master Student; Faculty Member; Department of Industrial Engineering; College of Sciences; Graduate School of Sciences and Engineering; College of Engineering; 28405; N/A; 32863In this study, we consider the coordination of transportation and production policies between a single supplier and a single retailer in a deterministic inventory system. In this supply chain, the customers are willing to wait at the expense of a waiting cost. Accordingly, the retailer does not hold inventory but accumulates the customer orders and satisfies them at a later time. The supplier produces the items, holds the inventory and ships the products to the retailer to satisfy the external demand. We investigate both a coordinated production/transportation model and a decentralized model. In the decentralized model, the retailer manages his own system and sends orders to the supplier, while the supplier determines her own production process and the amount to produce in an inventory replenishment cycle according to the order quantity of the retailer. However, in the coordinated model, the supplier makes all the decisions, so that she determines the length of the replenishment and transportation cycles as well as the shipment quantities to the retailer. We determine the structure of the optimal replenishment and transportation cycles hi both coordinated and decentralized models and the corresponding costs. Our computational results compare the optimal costs under the coordinated and decentralized models. We also numerically investigate the effects of several parameters on the optimal solutions.Publication Metadata only A data-driven optimization framework for routing mobile medical facilities(2020) Yücel, Eda; Bozkaya, Burçin; Gökalp, Cemre; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage.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 dynamic inventory rationing problem with uncertain demand and production rates(Springer, 2015) Turgay, Zeynep; Department of Industrial Engineering; Department of Industrial Engineering; Karaesmen, Fikri; Örmeci, Lerzan; Faculty Member; Faculty Member; Department of Industrial Engineering; College of Engineering; College of Engineering; 3579; 32863We investigate the structural properties of a finite horizon, discrete time single product inventory rationing problem, where we allow random replenishment (production) opportunities. In contrast to the standard models of dynamic capacity control in revenue management or production/inventory systems, we assume that the demand/production rates are not known with certainty but lie in some interval. To address this uncertainty, we formulate a robust stochastic dynamic program and show how the structural properties of the optimal policy propagate to the robust counterpart of the problem. Further, we explore how the optimal policy changes with respect to the uncertainty set. We also show that our results can be extended to certain alternative robust formulations.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.