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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6
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Publication Open Access A hierarchical solution approach for a multicommodity distribution problem under a special cost structure(Elsevier, 2012) Koca, Esra; Department of Industrial Engineering; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; College of EngineeringMotivated by the spare parts distribution system of a major automotive manufacturer in Turkey, we consider a multicommodity distribution problem from a central depot to a number of geographically dispersed demand points. The distribution of the items is carried out by a set of identical vehicles. The demand of each demand point can be satisfied by several vehicles and a single vehicle is allowed to serve multiple demand points. For a given vehicle, the cost structure is dictated by the farthest demand point from the depot among all demand points served by that vehicle. The objective is to satisfy the demand of each demand point with the minimum total distribution cost. We present a novel integer linear programming formulation of the problem as a variant of the network design problem. The resulting optimization problem becomes computationally infeasible for real-life problems due to the large number of integer variables. In an attempt to circumvent this disadvantage of using the direct formulation especially for larger problems, we propose a Hierarchical Approach that is aimed at solving the problem in two stages using partial demand aggregation followed by a disaggregation scheme. We study the properties of the solution returned by the Hierarchical Approach. We perform computational studies on a data set adapted from a major automotive manufacturer in Turkey. Our results reveal that the Hierarchical Approach significantly outperforms the direct formulation approach in terms of both the running time and the quality of the resulting solution especially on large instances.Publication Open Access Rounding on the standard simplex: regular grids for global optimization(Springer, 2014) Bomze, Immanuel M.; Gollowitzer, Stefan; Department of Industrial Engineering; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; College of EngineeringGiven a point on the standard simplex, we calculate a proximal point on the regular grid which is closest with respect to any norm in a large class, including all l(p)-norms for p >= 1 . We show that the minimal l(p)-distance to the regular grid on the standard simplex can exceed one, even for very fine mesh sizes in high dimensions. Furthermore, for p = 1, the maximum minimal distance approaches the l(1)-diameter of the standard simplex. We also put our results into perspective with respect to the literature on approximating global optimization problems over the standard simplex by means of the regular grid.Publication Open Access Sustainability in supply chain management: aggregate planning from sustainability perspective(Public Library of Science, 2016) Saraçoğlu, O.; Arslan, M.C.; Department of Industrial Engineering; Türkay, Metin; Saraçoğlu, Öztürk; Arslan, Mehmet Can; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956; N/A; N/ASupply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.Publication Open Access Joint gateway selection, transmission slot assignment, routing and power control for wireless mesh networks(Elsevier, 2013) Gökbayrak, Kağan; Department of Industrial Engineering; Yıldırım, Emre Alper; Faculty Member; Department of Industrial Engineering; College of EngineeringWireless mesh networks (WMNs) provide cost effective solutions for setting up a communications network over a certain geographic area. In this paper, we study strategic problems of WMNs such as selecting the gateway nodes along with several operational problems such as routing, power control, and transmission slot assignment. Under the assumptions of the physical interference model and the tree-based routing restriction for traffic flow, a mixed integer linear programming (MILP) formulation is presented, in which the objective is to maximize the minimum service level provided at the nodes. A set of valid inequalities is derived and added to the model in an attempt to improve the solution quality. Since the MILP formulation becomes computationally infeasible for larger instances, we propose a heuristic method that is aimed at solving the problem in two stages. In the first stage, we devise a simple MILP problem that is concerned only with the selection of gateway nodes. In the second stage, the MILP problem in the original formulation is solved by fixing the gateway nodes from the first stage. Computational experiments are provided to evaluate the proposed models and the heuristic method.Publication Open Access Temperature effects on grinding residual stress(Elsevier, 2014) Fergani, Omar; Shao, Yamin; Liang, Steven Y.; Department of Mechanical Engineering; Lazoğlu, İsmail; Faculty Member; Department of Mechanical Engineering; College of Engineering; 179391Residual stress is a key factor that influences the reliability, precision, and life of final products. Earlier studies have alluded to the fact that the grinding process is usually the source of a tensile residual stress on the part surface, while there exists a temperature level commonly referred to as the onset tensile temperature beyond which the tensile profile of residual stresses starts to be generated. In this paper, a physics-based model is proposed to predict the onset temperature as a function of residual stress on an analytical and quantitative basis. The predictive model is based on the temperature distribution function using a moving heat source approach. Then, the thermal stresses are calculated analytically using Timoshenko thermal stress theory [1] followed by an elastic-plastic relaxation condition imposed on these stresses, thus leading to the resulting residual stresses. The model-predicted results have been experimentally validated using data of the grinding of AISI52100 hardened steel with subsequent X-ray and Neutron diffraction measurements. The model was shown to predict the residual stress profile under given process conditions and material properties, therefore providing an analytical tool for grinding process planning and optimization based on the understanding of onset tensile temperature for control of tensile residual stresses. (C) 2014 Elsevier B.V.Publication Open Access Machining forces and tool deflections in micro milling(Elsevier, 2013) Department of Mechanical Engineering; Mamedov, Ali; Khavidaki, Sayed Ehsan Layegh; Lazoğlu, İsmail; Researcher; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 179391The analysis of cutting forces plays an important role for investigation of mechanics and dynamics of cutting process. The importance of force analysis is due to its major role in surface quality of machined parts. Presented force model calculates instantaneous chip thickness by considering trajectory of the tool tip while tool rotates and moves ahead continuously. The model also takes plowing force component into consideration relating it to elastic recovery based on interference volume between tool and workpiece. Based on the mathematical model, distribution of the force acting on the tool is calculated. It is known that this force will create deflection of the tool during cutting, which will result in imperfections of the final part. From this point of view, it is important to predict tool deflections in order to control the cutting process and to avoid failure of the tool. Both force and deflection models are validated on Aerospace Aluminum Alloy (Al-7050), through micro end milling experiments for a wide range of cutting conditions using micro dynamometer and laser displacement sensors. (C) 2013 The Authors. Published by Elsevier B.V.Publication Open Access Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation(BioMed Central, 2016) Department of Industrial Engineering; Gönen, Mehmet; Faculty Member; Department of Industrial Engineering; College of Engineering; 237468Identifying molecular signatures of disease phenotypes is studied using two mainstream approaches: (i) Predictive modeling methods such as linear classification and regression algorithms are used to find signatures predictive of phenotypes from genomic data, which may not be robust due to limited sample size or highly correlated nature of genomic data. (ii) Gene set analysis methods are used to find gene sets on which phenotypes are linearly dependent by bringing prior biological knowledge into the analysis, which may not capture more complex nonlinear dependencies. Thus, formulating an integrated model of gene set analysis and nonlinear predictive modeling is of great practical importance. In this study, we propose a Bayesian binary classification framework to integrate gene set analysis and nonlinear predictive modeling. We then generalize this formulation to multitask learning setting to model multiple related datasets conjointly. Our main novelty is the probabilistic nonlinear formulation that enables us to robustly capture nonlinear dependencies between genomic data and phenotype even with small sample sizes. We demonstrate the performance of our algorithms using repeated random subsampling validation experiments on two cancer and two tuberculosis datasets by predicting important disease phenotypes from genome-wide gene expression data. We are able to obtain comparable or even better predictive performance than a baseline Bayesian nonlinear algorithm and to identify sparse sets of relevant genes and gene sets on all datasets. We also show that our multitask learning formulation enables us to further improve the generalization performance and to better understand biological processes behind disease phenotypes.Publication Open Access Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times(Taylor _ Francis, 2021) Department of Business Administration; N/A; Tan, Barış; Khayyati, Siamak; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 28600; N/ADeveloping efficient performance evaluation methods is important to design and control complex production systems effectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under different service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity buffers. SLQNA yields the mean, coefficient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating different blocks for different parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and efficient method to evaluate the cycle time distribution and other performance measures in queueing networks.Publication Open Access A new identification method of specific cutting coefficients for ball end milling(Elsevier, 2014) Department of Mechanical Engineering; Khavidaki, Sayed Ehsan Layegh; Lazoğlu, İsmail; Faculty Member; Department of Mechanical Engineering; Manufacturing and Automation Research Center (MARC); Graduate School of Sciences and Engineering; College of Engineering; N/A; 179391The paper presents a new and accurate strategy for estimation of cutting coefficients for ball-end milling of free form surfaces in 3- and 5-axis operations. Since the cutting coefficients are not constant along the tool axis in the ball part of the cutter, the tool is considered by dividing the ball region into thin disks. In order to find the contribution of each disk to resultant cutting force, an experimental setup is designed to cut the workpiece while only that disk is in engaged with the workpiece. It is shown that this method is more efficient than common methods of mechanistic identification of cutting constants that are available in literature. The derivations are improved by considering the helix angle and cutting edge length to enhance the accuracy of the estimated cutting coefficients. Validation of the proposed strategy is demonstrated experimentally by simulation of cutting forces and comparing the results with conventional methods of identification of cutting coefficients that have been proposed in the literature. (C) 2014 Elsevier B.V.