A Nonlinear Programming Solution to the Dual Response Problem

Castillo, Enrique Del; Montgomery, Douglas C.

doi:10.1080/00224065.1993.11979454

Cited by 251 publications

(97 citation statements)

References 4 publications

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“…This approach is one of the basic approaches used in the multiple-objective decision-making problem. An example to such approaches can be found in the literature of response surface methodology (RSM) [3,9]. According to Kim et al [10], the main disadvantage of these approaches is that they do not conform to the basic idea of multiresponse surface optimization to simultaneously consider all of the responses.…”

Section: Literature Reviewmentioning

confidence: 99%

An artificial neural network approach to multiple-response optimization

Noorossana

Tajbakhsh

Saghaei

2008

Int J Adv Manuf Technol

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In many manufacturing cases, engineers are required to optimize a number of responses simultaneously. A common approach for the optimization of multipleresponse problems begins with using polynomial regression models to estimate the relationships between responses and control factors. Then, a technique for combining different response functions into a single scalar, such as a desirability function, is employed and, finally, an optimization method is used to find the best settings for the control factors. However, in certain cases, relationships between responses and control factors are far too complex to be efficiently estimated by polynomial regression models. In addition, in many manufacturing cases, engineers encounter qualitative responses, which cannot be easily stated in the form of numbers. An alternative approach proposed in this paper is to use an artificial neural network (ANN) to estimate the quantitative and qualitative response functions. In the optimization phase, a genetic algorithm (GA) is considered in conjunction with an unconstrained desirability function to determine the optimal settings for the control factors. Two manufacturing examples in which engineers were asked to optimize multiple responses from the semiconductor and textile industries are included in this article. The results indicate the strength of the proposed approach in the optimization of multiple-response problems.

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Section: Literature Reviewmentioning

confidence: 99%

An artificial neural network approach to multiple-response optimization

Noorossana

Tajbakhsh

Saghaei

2008

Int J Adv Manuf Technol

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“…For the optimisation of duel responses, Montgomery and Castillo (1993) suggested a non-linear programming solution. Myers and Carter (1973) proposed response surface techniques.…”

Section: Simultaneous Optimisation Of Response Variablesmentioning

confidence: 99%

Application of desirability function for optimizing the performance characteristics of carbonitrided bushes

John¹

2013

10.5267/j.ijiec

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The performance of a product is generally characterized by more than one response variable. Hence the management often faces the problem of simultaneous optimization of many response variables. This study was undertaken to simultaneously optimize the surface hardness and case depth of carbonitrided bushes. Even though lots of literature has been published on various methodologies for tackling the multi-response optimization problem, the simultaneous optimization of heat treated properties of carbonitrided bushes are not reported yet. In this research the effect of four factors and two interactions on surface hardness and case depth of carbontirded bushes were studied using design of experiments. Based on the experimental results, the expected values of the heat treated properties of the bushes were estimated for all possible combination of factors. Then the best combination which, simultaneously optimized the response variables, was arrived at using desirability function. The study showed that the optimum combination obtained through desirability function approach not only minimized the variation around the targets of surface hardness and case depth but also was superior to the ones obtained by optimizing the response variables separately. Moreover this study provides a useful and effective approach to design the production process to manufacture bushes with customer specified surface hardness and case depth targets.

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“…There are several ways to draw the optimal solution considering multiple individual desirability functions [22][23][24][25][26] but the most widely used method is to optimize by converting multiple desirability functions into single desirability. In most cases the concept of geometric means is often used for this aggregation.…”

Section: Desirability Function Approachmentioning

confidence: 99%

An Interactive Multiobjective Optimization Approach to Supplier Selection and Order Allocation Problems Using the Concept of Desirability

Lee

Kang

2018

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Abstract:In supply chain management, selecting the right supplier is one of the most important decision-making processes for improving corporate competitiveness. In particular, when a buyer considers selecting multiple suppliers, one should consider the issue of order allocation with supplier selection. In this article, an interactive multiobjective optimization approach is proposed for the supplier selection and order allocation problem. Also, the concept of desirability is incorporated into the optimization model to take into account the principles of diminishing marginal utility. The results are presented by comparing them with the solutions from the weighting methods. This study shows the advantage of the proposed method in that the decision-maker directly checks the degree of desirability and learns his/her preference structure through improved solutions.

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A Nonlinear Programming Solution to the Dual Response Problem

Cited by 251 publications

References 4 publications

An artificial neural network approach to multiple-response optimization

An artificial neural network approach to multiple-response optimization

Application of desirability function for optimizing the performance characteristics of carbonitrided bushes

An Interactive Multiobjective Optimization Approach to Supplier Selection and Order Allocation Problems Using the Concept of Desirability

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