A note on the effect of neighborhood structure in simulated annealing
Computers and Operations Research
Optimization of a multi-response problem in Taguchi's dynamic system
Computers and Industrial Engineering
A review of robust optimal design and its application in dynamics
Computers and Structures
Process optimization of gold stud bump manufacturing using artificial neural networks
Expert Systems with Applications: An International Journal
Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments
Applied Soft Computing
Artificial neural network based approach for dynamic parameter design
Expert Systems with Applications: An International Journal
Optimizing time limits for maximum sales response in Internet shopping promotions
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Obtaining industrial experimental designs using a heuristic technique
Expert Systems with Applications: An International Journal
Implementation of a genetic algorithm on MD-optimal designs for multivariate response surface models
Expert Systems with Applications: An International Journal
Application of the Box-Behnken design to the optimization of process parameters in foam cup molding
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Desirability improvement of committee machine to solve multiple response optimization problems
Advances in Artificial Neural Systems
Hi-index | 12.06 |
To simultaneously optimize the parameter robust design of dynamic multiple responses is difficult due to product complexity; however, the design is what determines most of the production time, cost, and quality. Although several methods tackling this problem have been published, they have proven unable to effectively resolve the situation if a system has continuous control factors. This work proposes a data mining approach, consisting of four stages based on artificial neural networks (ANN), desirability functions, and a simulated annealing (SA) algorithm to resolve the problems of dynamic parameter design with multiple responses. An ANN is employed to build a system's response function model. Desirability functions are used to evaluate the performance measures of multiple responses. A SA algorithm is applied to obtain the best factor settings through the response function model. By using the proposed approach, the obtained best factor settings can be any values within their upper and lower bounds so that the system's multiple responses have the least sensitivity to noise factors along the magnitude of the signal factor. An example from the literature is illustrated to confirm the feasibility and effectiveness of the proposed approach.