Statistical and Optimization Techniques for Laundry Portfolio Optimization at Procter & Gamble
Published Online:20 Oct 2015https://doi.org/10.1287/inte.2015.0802
References
- ASTM International (2014) Standard Guide for Evaluating Stain Removal Performance in Home Laundering (ASTM International, West Conshohocken, PA).Google Scholar
- (2004) Pooling problem: Alternate formulations and solution methods. Management Sci. 50(6):761–776.Link, Google Scholar
- (2014) Observed benefits of the portfolio optimization approach provided via email communication with Natalie Esquejo, June.Google Scholar
- (1990) A history of mathematical programming in the petroleum industry. Interfaces 20(4):117–127.Link, Google Scholar
- (2005) Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed. (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2002) Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed. (Springer, New York).Google Scholar
- (2004) Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Methods Res. 33(2):261–304.Crossref, Google Scholar
- (2013) Analysis of MILP techniques for the pooling problem. Accessed April 1, 2015, http://www.optimization-online.org/DB_FILE/2013/04/3849.pdf.Google Scholar
- (1990) A decomposition strategy for global optimum search in the pooling problem. ORSA J. Comput. 2(3):225–235.Link, Google Scholar
- (2011) Optimal Design of Experiments: A Case Study Approach (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
- (2013) Pooling problem: Relaxations and discretizations. Accessed April 1, 2015, http://www.optimization-online.org/DB_FILE/2012/10/3658.pdf.Google Scholar
- (2011) An expository paper on optimal design. Quality Engrg. 23(3):287–301.Crossref, Google Scholar
- (2004) Applied Linear Statistical Models, 5th ed. (McGraw-Hill/Irwin, New York).Google Scholar
- (1990) Subset Selection in Regression (Chapman & Hall, London).Crossref, Google Scholar
- (1990) A global optimization algorithm (GOP) for certain classes of nonconvex NLPs—II. Applications of theory and test problems. Comput. Chemical Engrg. 14(12):1419–1434.Crossref, Google Scholar

