Fakülteler / Faculties

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Now showing 1 - 10 of 19
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    Simulation Optimization Based DSS Application: A Diamond Tool Production Line in İndustry
    (2006) Dengiz, B.; Bektas, T; Ultanir, AE; 0000-0003-0634-144X
    A diamond tool manufacturing system simulation is developed to predict the number of machines and the number of workers necessary to maintain desired levels of production for a company in Ankara, Turkey. The current manufacturing system is analysed by a simulation model emphasizing the bottlenecks and the poorly utilized machines. Validated simulation outputs are collected and used to build a multiple regression meta-model as a simulation optimization based decision support system (DSS). The proposed DSS involves analysis and evaluation of the system's behaviour through the use of a meta-model with an integrated optimization module. It enables the decision maker to perform sensitivity analysis by considering several combinations of decision variables. The aim of this study is two fold. The first is to represent a simulation optimization based DSS application for a real system by considering all the required steps. The second is to analyse the performance of the current production system and determine the optimum working conditions by simulation with greatly reduced cost, time, and effort. (c) 2005 Elsevier B.V. All rights reserved.
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    Redesign Of PCB Production Lıine With Simulation And Taguchi Design
    (2009) Dengiz, Berna
    This paper presents the problem of determining the optimum condition of printed circuit board (PCB) manufacturing process in an electronic company in Ankara,. Turkey. In the optimization stage of the study Taguchi method is integrated with simulation model considering minimum total cost under stochastic breakdowns. Using this methodology we investigate the system performance of the current PCB line and determine the optimum working conditions with reduced cost, time and effort.
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    Optimization of Manufacturing Systems Using A Neural Network Metamodel with A New Training Approach
    (2009) Dengiz, B.; Alabas-Uslu, C.; Dengiz, O.
    In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (tst_NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp NNM). Computational results show that tst NNM is superior to bp NNM for both of the manufacturing systems. Journal of the Operational Research Society (2009) 60, 1191-1197. doi:10.1057/palgrave.jors.2602620 Published online 30 July 2008
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    Buffer Allocation and Performance Modeling in Asynchronous Assembly System Operations: An Artificial Neural Network Metamodeling Approach
    (2007) Altiparmak, Fulya; Dengiz, Berna; Bulgak, Akif A.; 0000-0003-1730-4214; AAF-7020-2021
    This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems ( AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network ( ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach. (c) 2006 Elsevier B. V. All rights reserved.
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    An International Facility Design Project
    (2008) Lacksonen, Thomas; Dengiz, Berna
    This paper describes an international facilities design project for Manufacturing and Industrial Engineering students. American and Turkish engineering students collaborated to create and implement the re-design of a Turkish wheelchair manufacturing facility. The company needed engineering assistance to improve the efficiency and increase the capacity of their existing factory. Turkish Industrial Engineering students went on-site to collect data and draw the existing facility layout. American Manufacturing Engineering students analyzed the data and developed new layout designs. Four American students traveled to Turkey between semesters to implement the initial phases of their design. In the second semester, the Turkish students simulated the new layout to see the performance improvements, completing their project. Student learning outcomes were positive for both groups of students. The paper explains critical steps in identifying projects and partners. Lessons are shown about successes and shortcomings in planning, operating, and communicating with design teams across cultures.
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    Aintshop Production Line Optimization Using Response Surface Methodology
    (2007) Dengiz, Berna; Belgin, Onder; 0000-0001-6702-2608; K-1080-2019
    This paper deals with the problem of determining the optimum number of workstations to be used in parallel and workers at some stations using simulation optimization approach in a paint shop line of an automotive factory in Ankara, Turkey. In the optimization stage of the study Response Surface Methodology (RSM) is used to find the optimum levels of considered factors. Simulation model and optimization stage integration is used both to analyse the performance of the current paint shop line and determine the optimum working conditions, respectively, with reduced cost, time and effort.
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    A Tabu Search Algorithm for the Training of Neural Networks
    (2009) Dengiz, B.; Alabas-Uslu, C.; Dengiz, O.
    The most widely used training algorithm of neural networks (NNs) is back propagation ( BP), a gradient-based technique that requires significant computational effort. Metaheuristic search techniques such as genetic algorithms, tabu search (TS) and simulated annealing have been recently used to cope with major shortcomings of BP such as the tendency to converge to a local optimal and a slow convergence rate. In this paper, an efficient TS algorithm employing different strategies to provide a balance between intensification and diversification is proposed for the training of NNs. The proposed algorithm is compared with other metaheuristic techniques found in literature using published test problems, and found to outperform them in the majority of the test cases.
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    A Systematic Approach to Reduce Human and System-related Errors Causing Customer Dissatisfaction in a Production Environment
    (2009) Pakdil, Fatma; Oezkoek, Onur; Dengiz, Berna; Kara, Imdat; Selvi, Nilay; Karg, Alper; ABH-1078-2021
    In this study, a systematic methodology for business process improvement, which aims to eliminate human and system-related errors resulting in customer dissatisfaction in a production environment, is presented. The proposed methodology consists of problem identification and analysis, preventing human-related errors and system-related error steps respectively. The methodology was also implemented in a real-life organisation. Current and proposed systems are compared via a simulation model to examine the results of process improvements. The case study shows that the proposed methodology works exceedingly well and yields considerable improvement in the process under study. The most important and impressive difference of this paper from the previous literature is that process improvement needs are derived directly from customer dissatisfaction reasons and solved by the proposed systematic methodology. In this way human-related and system-related errors were perceived opportunities for improvement.
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    A Hybrid Simulated Annealing For A Multi-Objective Stochastic Assembly Line Balancing Problem
    (2008) Cakir, Burcin; Dengiz, Berna; Altiparmak, Fulya; Xia, GP; Deng, XQ; 0000-0003-1730-4214; AAF-7020-2021
    Asssembly line balancing is the problem of assigning tasks to the workstations, while optimizing one or more objectives without violating restrictions imposed on the line. In practice, task times may be random due to the worker fatigue, low skill levels, job dissatisfaction, poorly maintained equipment, defects in raw material, etc. When stochastic task times are taken consideration in assembly lines, balancing procedure is more complex due to the probability of incompleteness of stations times in a given cycle time. In this study, a multi-objective simulated annealing algorithm (m_SAA) is proposed for single-model, stochastic assembly line balancing problem with the aim of minimizing of smoothness index and total design cost. To obtain Pareto-optimal solutions, m_SAA implements tabu list and a multinomial probability mass function approach. The effectiveness of the proposed m_SAA is comparatively investigated using another SA using weight-sum approach on the test problems. Computational results show that m_SAA with multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of quality of Pareto-optimal solutions.