Abstract
The selection of confirmed key system input (KIV) settings is the main outcome of a six sigma project. The term “optimization problem” refers to the selection of settings to derive to formally maximize or minimize a quantitative objective. Chapter 6 described how formal optimization methods are sometimes applied in the assumption phase of projects to develop recommended settings to be evaluated in the control or verify phases.
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References
Aizawa AN, Wah BW (1994) Scheduling of genetic algorithms in a noisy environment. Evol Comput 2:97–122
Allen TT, Bernshteyn M (2003) Supersaturated designs that maximize the probability of finding the active factors. Technometrics 45:1–8
Andradóttir S (1998) A review of simulation optimization techniques. In: Proceedings of the 1998 winter simulation conference, Washington, DC
Bernshteyn M (2001) Simulation optimization methods that combine multiple comparisons and genetic algorithms with applications in design for computer and supersaturated experiments. Ph.D. dissertation. The Ohio State University, Columbus, Ohio
Boesel J (1999) Search and selection for large-scale stochastic optimization . Ph.D. dissertation. Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois
Chelouah R, Siarry P (2000) A continuous genetic algorithm designed for the global optimization of multimodal functions. J Heuristics 6:191–213
De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. Ph.D. dissertation. The University of Michigan, Ann Arbor
Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
Ittiwattana W (2002) A method for simulation optimization with applications in robust process design and locating supply chain operations. Ph.D. dissertation. The Ohio State University, Columbus, Ohio
Mühlenbein H, Schomish M, Born J (1991) The parallel genetic algorithm as function optimizer. In: Proceedings of the international conference on genetic algorithms, pp 271–278
Papadimitriou CH (1994) Computational complexity. Addison-Wesley, Boston, MA
Rechenberg I (1964) Kybernetische losungsansteuerung einer experimentellen forschungsaufgabe. Hermann-Fottinger-Institut fur Stromungstechnik der Technische Universitat, Berlin, Seminarvortrag
Rudolph G (1996) Convergence of evolutionary algorithms in general search spaces. In: Proceedings of the third IEEE conference on evolutionary computation. IEEE Press, Piscataway, NJ, pp 50–54
Swisher TR, Hyden PD, Jacobson SH, Schruben LW (2000) A survey of simulation optimization techniques and procedures. In: Joines JA, Barton RR, Kang K, Fishwick PA. Proceedings of the 2000 winter simulation conference, vol 1, pp 119–128
Yu L (2000) Expected modeling errors and low cost response surface methods. Ph.D. dissertation. The Ohio State University, Columbus, Ohio
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Allen, T.T. (2019). Optimization and Strategy. In: Introduction to Engineering Statistics and Lean Six Sigma. Springer, London. https://doi.org/10.1007/978-1-4471-7420-2_19
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DOI: https://doi.org/10.1007/978-1-4471-7420-2_19
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