• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.3
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• pp.49-61
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• 2015

We study the problem of multiple response optimization (MRO) and focus on the selection of input levels which will produce desirable output quality. We propose an interactive multiple objective optimization approach to the input design. The earlier interactive methods utilized for MRO communicate with the decision maker only using the response variable values, in order to improve the current response values, thereby resulting in the corresponding design solution automatically. In their interaction steps of preference articulation, no account is taken of any active changes in design variable values. On the contrary, our approach permits the decision maker to change the design variable values in its interaction stage, which makes possible the consideration of the preference or economics of the design variable side. Using some typical value functions, we also demonstrate that our method converges reasonably well to the known optimal solutions.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.46-57
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• 2006

The desirability function approach to multiple response optimization is a useful technique for the analysis of experiments in which several responses are optimized simultaneously. But the existing methods have some defects, and have to be modified to some extent. This paper proposes a new method to combine the individual desirabilities.

• Journal of the Korea Society for Simulation
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• v.3 no.2
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• pp.57-67
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• 1994

For many practical and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. In this paper, with discussion of simulation optimization techniques, a case study in machining process for application of simulation optimization is presented. Most of optimization techniques can be classified as single-or multiple-response techniques. The optimization of single-response category, these strategies are gradient based search methods, stochastic approximate method, response surface method, and heuristic search methods. In the multiple-response category, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphical method, direct search method, constrained optimization, unconstrained optimization, and goal programming methods. The choice of the procedure to employ in simulation optimization depends on the analyst and the problem to be solved.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.164-175
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• 2017

Dual response surface optimization (DRSO) attempts to optimize mean and variability of a process response variable using a response surface methodology. In general, mean and variability of the response variable are often in conflict. In such a case, the process engineer need to understand the tradeoffs between the mean and variability in order to obtain a satisfactory solution. Recently, a Posterior preference articulation approach to DRSO (P-DRSO) has been proposed. P-DRSO generates a number of non-dominated solutions and allows the process engineer to select the most preferred solution. By observing the non-dominated solutions, the DM can explore and better understand the trade-offs between the mean and variability. However, the non-dominated solutions generated by the existing P-DRSO is often incomprehensive and unevenly distributed which limits the practicability of the method. In this regard, we propose a modified P-DRSO using multiple objective genetic algorithms. The proposed method has an advantage in that it generates comprehensive and evenly distributed non-dominated solutions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1256-1261
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• 2004

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. It is more expensive to carry out nonlinear response optimization than linear response optimization. The conventional method spends most of the total design time on nonlinear analysis. Thus, the NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The function satisfies the descent condition at each cycle and the NROESL algorithm converges. It is mathematically validated that the solution of the algorithm satisfies the Karush-Kuhn-Tucker necessary condition of the original nonlinear response optimization problem. The NROESL algorithm is applied to two structural problems. Conventional optimization with sensitivity analysis using the finite difference method is also applied to the same examples. The results of the optimizations are compared. The proposed method is very efficient and derives good solutions.

• Journal of Integrative Natural Science
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• v.14 no.3
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• pp.73-77
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• 2021

Robust design is an approach to reduce the performance variation of mutiple responses in products and processes. In fact, in many experimental designs require the simultaneous optimization of multiple responses. In this paper, we propose how to simultaneously optimize multiple responses for robust design when data are collected from a combined array. The proposed method is based on the quadratic loss function. An example is illustrated to show the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1051-1060
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• 2005

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• Journal of Korean Society for Quality Management
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• v.39 no.3
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• pp.377-390
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• 2011

A common problem encountered in product or process design is the selection of optimal parameter levels which involves simultaneous consideration of multiple response variables. This is called a multiresponse problem. A multiresponse problem is solved through three major stages: data collection, model building, and optimization. Up to date, various methods have been proposed for the optimization, including the desirability function approach and loss function approach. In this paper, the existing studies in multiresponse optimization are reviewed and a future research direction is then proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.625-633
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• 2013

Multiple response surface optimization (MRSO) aims at finding a setting of input variables which simultaneously optimizes multiple responses. The minimization of mean squared error (MSE), which consists of the squared bias and variance terms, is an effective way to consider the location and dispersion effects of the responses in MRSO. This approach basically assumes that both the terms have an equal weight. However, they need to be weighted differently depending on a problem situation, for example, in case that they are not of the same importance. This paper proposes to use the weighted MSE (WMSE) criterion instead of the MSE criterion in MRSO to consider an unequal weight situation.