Adedeji B. Badiru, Oye Ibidapo-Obe, Babatunde J. Ayeni, Industrial control systems: mathematical and statistical models and techniques

CRC Press, 2012 - 380 p. -Discusses scientific uncertainty, fuzziness, and application of stochastic–fuzzy models in urban transit, water resources, energy planning, and education. Examines the methodical development of the optimization problem in relation to applications in applied sciences, from classical techniques through stochastic approaches to contemporary and recent methods of intelligent Meta Heuristics. Develops a generic project scheduling tool that incorporates resource characteristics and performance interdependencies among different resource groups, and then proceeds to map the most adequate resource units to each newly scheduled project activity
Explores the use of statistical control techniques for quality improvement in the manufacturing environment. Presents additional statistical tools for quality and process improvement, including factorial designs, response surface methodology, central composite designs, and response surface optimization. Covers how to use a Bayesian technique for parameter estimation.