Yazar "Toklu, B." seçeneğine göre listele

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    Öğe
    Analysis of bowl effects on assembly line using queueing networks and constraint programming procedure
    (Computers and Industrial Engineering, 2014) Pinarbaşı, M.; Alağad, H.M.; Yüzükirmizi, M.; Toklu, B.
    In this study, a new solution procedure based on queueing networks and constraint programming is proposed to model and solve the Assembly Line Balancing Problem (ALBP). Variation of the task and the station times, and precedence relation effects are considered to evaluate the line performance. Station utilization, total average number of jobs and smoothness index are used as performance measures. Bowl effect, inverted bowl effect and variability imbalance which are seen in balanced lines are examined by using proposed procedure. Also effects of the variability on the line performance are reviewed. Literature data sets are utilized to assess the effectiveness of the procedure.
  • [ X ]
    Öğe
    A non-linear programming model with fuzzy evaluations for customer satisfaction index estimation
    (Computers and Industrial Engineering, 2014) Aktepe, Adnan; Ersöz, S.; Toklu, B.
    Customer satisfaction index (CSI) is a cause-and-effect model of advanced customer satisfaction analysis. CSI models are used by several private and public institutions for developing key customer strategies throughout the world. Index values are based on predictions of customer evaluations. In the literature CSI is mostly modeled with linear statistical estimation methods. In a few of the studies, non-linear approach is used for estimation. Estimation of CSI with minimum error results in a more reliable and robust prediction. Therefore, in this study we propose a non-linear programming model for estimating CSI with fuzzy customer evaluations minimizing estimation errors. The estimation model brings significant contributions in this field of study. With the help of the model, we can find weights of measurement variables of a latent variable with minimized squared errors which is a key success factor in producing reliable indexes. In addition the model enables us to find coefficients of prediction equations that contribute to extend evaluation of index results. The model is also tested with data of a comprehensive survey application and application results are included.