Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, how...
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Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.
In this study, the authors proposed a solution for directly using genetic algorithms without linear conversion for solving nonlinear goal programming problems involving interval coefficients. The proposed technique in...
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In this study, the authors proposed a solution for directly using genetic algorithms without linear conversion for solving nonlinear goal programming problems involving interval coefficients. The proposed technique involves directly computing left-side interval numbers in the goal constraint equation from the genetic algorithm solution and comparing their size with those of the right-side numbers, for obtaining new difference variables. These difference variables vary depending on the goal type;they are defined using weights which express interval order evaluation criteria. In addition, as an example of application field for this method, the authors studied a large-scale problem for optimal design of system reliability involving interval coefficients, in order to clarify localization of this method. (C) 2002 Wiley Periodicals, Inc.
Traditional formulations on reliability optimization problems have assumed that the coefficients of models are known as fixed quantities and reliability design problem is treated as deterministic optimization problems...
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Traditional formulations on reliability optimization problems have assumed that the coefficients of models are known as fixed quantities and reliability design problem is treated as deterministic optimization problems. Because that the optimal design of system reliability is resolved in the same stage of overall system design, model coefficients are highly uncertainty and imprecision during design phase and it is usually very difficult to determine the precise values for them. However, these coefficients can be roughly given as the intervals of confidence. In this paper, we formulated reliability optimization problem as nonlinear goal programming with interval coefficients and develop a genetic algorithm to solve it. The key point is how to evaluate each solution with interval data. We give a new definition on deviation variables which take interval relation into account. Numerical example is given to demonstrate the efficiency of the proposed approach. (C) 1997 Elsevier Science Ltd.
goalprogramming (GP) is one of powerful techniques for solving multi-objective optimization and has been applied to various real-life problems. This paper presents an evolution program for solving nonlineargoal prog...
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goalprogramming (GP) is one of powerful techniques for solving multi-objective optimization and has been applied to various real-life problems. This paper presents an evolution program for solving nonlinear goal programming problems.
A nonlinear goal programming model is developed for the loading problem in a flexible maunfacturing system. A sequential search approach is used to obtain the solution. An example is presented to illustrate the applic...
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In this paper, we cast the problem of income redistribution in two different ways, one as a nonlinear goal programming model and the other as a game theoretic model. These two approaches give characterizations for the...
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In this paper, we cast the problem of income redistribution in two different ways, one as a nonlinear goal programming model and the other as a game theoretic model. These two approaches give characterizations for the probabilistic approach suggested by Intriligator for this problem. All three approaches reinforce the linear income redistribution plan as a desirable mechanism of income redistribution.
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