{"product_id":"process-optimization-a-statistical-approach-9780387714349","title":"Process Optimization: A Statistical Approach","description":"\u003cp\u003e • Author(s): Enrique del Castillo\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Industrial Engineering\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003cem\u003e \u003cp\u003ePROCESS OPTIMIZATION: A Statistical Approach\u003c\/p\u003e\u003c\/em\u003e is a textbook for a course in experimental optimization techniques for industrial production processes and other \"noisy\" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor\/electronics manufacturing and in biotech manufacturing industries.\u003c\/p\u003e \u003cp\u003eThe major features of \u003cem\u003ePROCESS OPTIMIZATION: A Statistical Approach\u003c\/em\u003e are: \u003c\/p\u003e \u003cul\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eIt provides a complete exposition of mainstream experimental design techniques, including designs for first and second order models, response surface and optimal designs;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eDiscusses mainstream response surface method in detail, including unconstrained and constrained (i.e., ridge analysis and dual and multiple response) approaches;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eIncludes an extensive discussion of Robust Parameter Design (RPD) problems, including experimental design issues such as Split Plot designs and recent optimization approaches used for RPD;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003ePresents a detailed treatment of Bayesian Optimization approaches based on experimental data (including an introduction to Bayesian inference), including single and multiple response optimization and model robust optimization;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eProvides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization and more;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eContains a discussion on robust optimization methods as used in mathematical programming and their application in response surface optimization;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eOffers software programs written in MATLAB and MAPLE to implement Bayesian and frequentist process optimization methods;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eProvides an introduction to the optimization of computer and simulation experiments including and introduction to stochastic approximation and stochastic perturbation stochastic approximation (SPSA) methods;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\n\u003cli\u003eIncludes an introduction to Kriging methods and experimental design for computer experiments;\u003c\/li\u003e \u003cp\u003e\u003c\/p\u003e\n\u003c\/ul\u003e \u003cp\u003eProvides extensive appendices on Linear Regression, ANOVA, and Optimization Results.\u003c\/p\u003e \u003cp\u003e\u003cbr\u003e \u003c\/p\u003e","brand":"Springer","offers":[{"title":"Hardcover","offer_id":45273708855447,"sku":"9780387714349","price":3639.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780387714349.webp?v=1769237348","url":"https:\/\/atlanticbooks.com\/products\/process-optimization-a-statistical-approach-9780387714349","provider":"Atlantic Books","version":"1.0","type":"link"}