Citations (26)
Keep up to date with the latest research on this topic with citation updates for this article.
Read on this site (8)
A D MacCalman, H Vieira & T Lucas. (2017) Second-order nearly orthogonal Latin hypercubes for exploring stochastic simulations. Journal of Simulation 11:2, pages 137-150.
Read now
Read now
PaulL. Goethals & Byung
Rae Cho. (2012) Designing the optimal process mean vector for mixed multiple quality characteristics. IIE Transactions 44:11, pages 1002-1021.
Read now
Read now
Eduardo Santiago. (2012) Design of robust parameter experiments in a continuous space using an evolutionary optimization algorithm. Journal of Statistical Computation and Simulation 82:6, pages 825-847.
Read now
Read now
Tae-Yeon Cho, DouglasC. Montgomery & ConnieM. Borror. (2012) A Case Study Involving Mixture–Process Variable Experiments within a Split-Plot Structure. Quality Engineering 24:1, pages 80-93.
Read now
Read now
Myrta Rodríguez, Bradley Jones, Connie M. Borror & Douglas C. Montgomery. (2010) Generating and Assessing Exact G-Optimal Designs. Journal of Quality Technology 42:1, pages 3-20.
Read now
Read now
Enrique Del Castillo, María Jesús Alvarez, Laura Ilzarbe & Elizabeth Viles. (2007) A New Design Criterion for Robust Parameter Experiments. Journal of Quality Technology 39:3, pages 279-295.
Read now
Read now
Peter J. Chung, Heidi B. Goldfarb & Douglas C. Montgomery. (2007) Optimal Designs for Mixture-Process Experiments with Control and Noise Variables. Journal of Quality Technology 39:3, pages 179-190.
Read now
Read now
Stefan H. Steiner, Michael Hamada, Bethany J. Giddings White, Vadim Kutsyy, Sofia Mosesova & Geoffrey Salloum. (2007) A Bubble Mixture Experiment Project for Use in an Advanced Design of Experiments Class. Journal of Statistics Education 15:1.
Read now
Read now
Articles from other publishers (18)
Adriana Menchaca-Méndez, Saúl Zapotecas-Martínez, Luis Miguel García-Velázquez & Carlos A. Coello Coello. (2022) Uniform mixture design via evolutionary multi‐objective optimization. Swarm and Evolutionary Computation 68, pages 100979.
Crossref
Crossref
Krishan Lal, Upendra Kumar Pradhan & V. K. Gupta. 2020. Statistical Methods and Applications in Forestry and Environmental Sciences. Statistical Methods and Applications in Forestry and Environmental Sciences
193
212
.
Wasinee Pradubsri, Boonorm Chomtee & John J. Borkowski. (2019) Using a genetic algorithm to generate D‐optimal designs for mixture‐process variable experiments. Quality and Reliability Engineering International 35:8, pages 2657-2676.
Crossref
Crossref
Wanida Limmun, Boonorm Chomtee & John J. Borkowski. (2019) The construction of robust mixture‐process experimental designs via genetic algorithm. Quality and Reliability Engineering International 35:6, pages 1582-1602.
Crossref
Crossref
C. Devon Lin, Christine M. Anderson-Cook, Michael S. Hamada, Leslie M. Moore & Randy R. Sitter. (2015) Using Genetic Algorithms to Design Experiments: A Review. Quality and Reliability Engineering International 31:2, pages 155-167.
Crossref
Crossref
Wanida Limmun, John J. Borkowski & Boonorm Chomtee. (2013) Using a Genetic Algorithm to Generate D-optimal Designs for Mixture Experiments. Quality and Reliability Engineering International 29:7, pages 1055-1068.
Crossref
Crossref
P.L. Goethals & B.R. Cho. (2012) The optimal process mean problem: Integrating predictability and profitability into an experimental factor space. Computers & Industrial Engineering 62:4, pages 851-869.
Crossref
Crossref
Byung Rae Cho & Sangmun Shin. (2012) Quality Improvement and Robust Design Methods to a Pharmaceutical Research and Development. Mathematical Problems in Engineering 2012, pages 1-14.
Crossref
Crossref
Paul L. Goethals & Byung Rae Cho. (2011) Reverse programming the optimal process mean problem to identify a factor space profile. European Journal of Operational Research 215:1, pages 204-217.
Crossref
Crossref
Aili Cheng, John Peterson & Pallavi Chitturi. (2011) A Confidence Region for Zero-Gradient Solutions for Robust Parameter Design Experiments. International Journal of Quality, Statistics, and Reliability 2011, pages 1-11.
Crossref
Crossref
Paul L. Goethals & Byung Rae Cho. (2011) Using higher precision-based response surface designs to determine the optimal process target. The International Journal of Advanced Manufacturing Technology 56:1-4, pages 13-30.
Crossref
Crossref
John A. Cornell. 2011. A Primer on Experiments with Mixtures. A Primer on Experiments with Mixtures
247
284
.
John A. Cornell. 2011. A Primer on Experiments with Mixtures. A Primer on Experiments with Mixtures
299
315
.
Byung Rae Cho, Yongsun Choi & Sangmun Shin. (2009) RETRACTED ARTICLE: Development of censored data-based robust design for pharmaceutical quality by design. The International Journal of Advanced Manufacturing Technology 49:9-12, pages 839-851.
Crossref
Crossref
Myrta Rodriguez, Douglas C. Montgomery & Connie M. Borror. (2009) Generating experimental designs involving control and noise variables using genetic algorithms. Quality and Reliability Engineering International 25:8, pages 1045-1065.
Crossref
Crossref
Byung Rae Cho, Sangmun Shin, Yongsun Choi & Jami Kovach. (2009) Development of a multidisciplinary optimization process for designing optimal pharmaceutical formulations with constrained experimental regions. The International Journal of Advanced Manufacturing Technology 44:9-10, pages 841-853.
Crossref
Crossref
K. Tahera, W. M. Chan & R. N. Ibrahim. (2007) Joint determination of process mean and production run: A review. The International Journal of Advanced Manufacturing Technology 39:3-4, pages 388-400.
Crossref
Crossref
Francisco Aparisi & J. Carlos García-Díaz. (2007) Design and optimization of EWMA control charts for in-control, indifference, and out-of-control regions. Computers & Operations Research 34:7, pages 2096-2108.
Crossref
Crossref