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- W4285018657 abstract "Simultaneous improvement of multiple quality characteristics is one of the main concerns in today's complex industrial systems. Multiresponse optimization enables us to address this concern and find a compromise solution of design factors that leads to desirable quality outputs. This problem becomes complicated when some degree of uncertainty affects the result of the experiments. In this paper, two sources of uncertainty are included in multi-response optimization problems. The first is experimental errors related to unknown variables, also called environmental nuisances, creating response surface modeling errors. The second source is errors in implementation. In the experimentation, the controllable factors are mainly set at some predefined levels, but after the optimal design has been found, it oscillates around that value during the implementation phase. To handle this problem, a scenario-based two-stage stochastic programming is developed in which desirability functions are aggregated to form the final objective function. Scenarios are generated by sampling from probability distribution functions of the response surfaces' coefficients under normality assumptions. The numerical results show that joint consideration of the error sources leads to a smaller range of desirability values among the different scenarios and a higher value of the total desirability index. • Implementation and modeling errors are included in multi-response optimization. • A scenario-based stochastic programming model is developed. • Correlation among all quality characteristics, including STB, NTB, and LTB, is allowed. • Robust parameter design is proposed considering robustness and randomness. • Shifting the optimal factor setting is modeled by a discrete set of scenarios." @default.
- W4285018657 created "2022-07-12" @default.
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- W4285018657 date "2022-09-01" @default.
- W4285018657 modified "2023-10-14" @default.
- W4285018657 title "A scenario-based desirability function for correlated multi-response optimization problems considering modeling and implementation errors" @default.
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- W4285018657 doi "https://doi.org/10.1016/j.jocs.2022.101764" @default.
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