Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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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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    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.