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    A multiobjective optimization method to environmental economic dispatch
    (ELSEVIER SCI LTD, 2007) Yalcinoz, Tankut; Koksoy, Onur
    An optimization technique based on progressive articulation of preference information is presented to solve the multiobjective environmental economic dispatch. For the multiobjective optimization problem, the use of weights to form a composite objective function reduces a multiple problem to a single problem. However, it also obviously "loses" some information in the conversion and this strategy is not expected to provide a robust solution or to even help trace the efficient frontier of solutions. Our main thrust is to facilitate a string of solutions of the problem without converting to the original problem to a simpler case. The proposed method handles the problem in an interactive way and does not need to know any global preference structure or some type of initial goals of the decision maker for the objectives. Numerical results for two case studies have been presented to illustrate the performance and applicability of the proposed method. The proposed method is compared with the genetic algorithm with arithmetic crossover and a neural network approach. (c) 2006 Elsevier Ltd. All rights reserved.
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    A nonlinear programming solution to robust multi-response quality problem
    (ELSEVIER SCIENCE INC, 2008) Koksoy, Onur
    Quite often, engineers obtain measurements associated with several response variables. Both the design and analysis of multi-response experiments with a focus on quality control and improvement have received little attention although they are sorely needed. In a multi-response case the optimization problem is more complex than in the single-response situation. In this paper we present a method to optimize multiple quality characteristics based on the mean square error (MSE) criterion when the data are collected from a combined array. The proposed method will generate more alternative solutions. The string of solutions and the trade-offs aid in determining the underlying mechanism of a system or process. The procedure is illustrated with an example, using the generalized reduced gradient (GRG) algorithm for nonlinear programming. (C) 2007 Elsevier Inc. All rights reserved.
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    An upside-down normal loss function-based method for quality improvement
    (TAYLOR & FRANCIS LTD, 2012) Koksoy, Onur; Fan, Shu-Kai S.
    Traditional measures of process quality do not offer much information on how much better or worse a process is when finding optimal settings of a given problem. The upside-down normal loss function (UDNLF) is a weighted loss function that provides a more reasonable risk assessment to the losses of being off-target in product engineering research. The UDNLF can be used in process design and optimization to accurately reflect and quantify the losses associated with the process in away which minimizes the expected loss of the upside-down normal (UDN). The function has a scale parameter which can be adjusted by the practitioners to account for the actual percentage of materials failing to work at specification limits. In this article, the 'target is best' case is addressed to estimate the expected loss of UDN due to variation from target in the robust process design and response surface modelling context. An approach is proposed to find the control factor settings of a system by directly minimizing the expected loss. The procedure and its merits are illustrated through an example.
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    MULTI-OBJECTIVE OPTIMIZATION SOLUTIONS TO THE TAGUCHI'S PROBLEM
    (Gazi Univ, 2005) Koksoy, Onur; Hocaoglu, Gulsum
    The dual response approach has widely been used in the literature to solve the Taguchi's problem. This excellent approach contains some deficiencies especially on the standard deviation response. These restrictions may rule out better conditions or globally preferred values in the industrial process or the product of interest. This paper presents a more flexible formulation of the problem and it's solving technique. In this study, we also extend the dual response problem by some cost elements (i.e., treatment cost).
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    Robust Design using Pareto type optimization: A genetic algorithm with arithmetic crossover
    (PERGAMON-ELSEVIER SCIENCE LTD, 2008) Koksoy, Onur; Yalcinoz, Tankut
    In dual response systems (DRSs) optimization restrictions on the secondary response may rule out better conditions, since an acceptable value for the secondary response is usually unknown. In fact, process conditions that result in a smaller standard deviation are often preferable. Recently, several authors stated that the standard deviation of any performance property could be treated as a new property in its own right as far as Pareto optimizer was concerned. By doing this, there will be many alternative solutions (i.e., the trade-offs between the mean and standard deviation responses) of the DRS problem and Pareto optimization can explore them all. Such analysis is useful, and that is required in order to achieve an improved understanding of the problem before searching for a final optimal solution. In this paper, we again follow this new philosophy and solve the DRS problem by using a genetic algorithm with arithmetic crossover. The genetic algorithm is applied to the printing process problem for improving the quality of a printing process. Genetic algorithms, in contrast to the one-solution-at-a-time approach of most optimization algorithms, maintain a population of hundreds, or thousands, of solutions in speedy manner. (C) 2007 Elsevier Ltd. All rights reserved.