TY  - BOOK
AU  - Sibalija,Tatjana V.
AU  - Majstorović,Vidosav D.
TI  - Advanced multiresponse process optimisation: an intelligent and integrated approach 
SN  - 9783319192550
AV  - TS183 .S53 2016
PY  - 2015///
CY  - Cham
PB  - Springer International Publishing
KW  - Expert systems (Computer science)
N1  - Foreword; -- Preface; -- Acknowledgments; -- Contents; -- Abbreviations and Symbols; -- 1 Introduction; -- Abstract; -- 1.1 Process Optimisation Based on Experimental Design; -- 1.1.1 Foundations of Taguchi's Method; -- 1.1.1.1 Orthogonal Arrays; -- 1.1.1.2 Robustness; -- 1.1.1.3 Quality Loss Function; -- 1.2 The Need for Advanced Multiresponse Process Optimisation in a Modern Industry; -- References; -- 2 Review of Multiresponse Process Optimisation Methods; -- Abstract; -- 2.1 Conventional Multiresponse Process Optimisation Approaches Based on Statistical Methods; -- 2.1.1 Response Surface Methodology; -- 2.1.2 Taguchi's Robust Parameter Design; -- 2.1.2.1 Multiresponse Optimisation Based on Engineering Experience and Knowledge About the Process in Taguchi Method; -- 2.1.2.2 Multiresponse Optimisation Based on the Assignment of Weigh Factors to Process Responses in Taguchi Method; -- 2.1.2.3 Multiresponse Optimisation Based on Regression Analysis in Taguchi Method; -- 2.1.2.4 Multiresponse Optimisation Based on Desirability Function Analysis in Taguchi Method; -- 2.1.2.5 Multiresponse Optimisation Based on Data Envelopment Analysis in Taguchi Method; --2.1.2.6 Multiresponse Optimisation Based on Principal Component Analysis in Taguchi Method; -- 2.1.2.7 Multiresponse Optimisation Based on Grey Relational Analysis in Taguchi Method; -- 2.1.2.8 Other Conventional Multiresponse Optimisation Approaches; -- 2.1.3 Multiresponse Optimisation Based on Goal-Programming; -- 2.2 Non-conventional Multiresponse Process Optimisation Approaches Based on Artificial Intelligence Techniques; -- 2.2.1 Multiresponse Optimisation Based on Fuzzy Multi-attribute Decision Making and Fuzzy Logic; -- 2.2.2 Multiresponse Optimisation Based on Artificial Neural Networks; -- 2.2.3 Multiresponse Optimisation Based on Metaheuristic Search Techniques; -- 2.2.3.1 Multiresponse Optimisation Based on Genetic Algorithm; -- 2.2.3.2 Multiresponse Optimisation Based on Simulated Annealing; -- 2.2.3.3 Multiresponse Optimisation Based on Particle Swarm Optimisation; -- 2.2.3.4 Multiresponse Optimisation Based on Ant Colony Optimisation; -- 2.2.3.5 Multiresponse Optimisation Based on Tabu Search; -- 2.2.3.6 Multiresponse Optimisation Based on Recently Developed Evolutionary Algorithms; -- Multiresponse Optimisation Based on Artificial Bee Colony Algorithm; -- Multiresponse Optimisation Based on Biogeography-Based Optimisation; -- Multiresponse Optimisation Based on Teaching--Learning-Based Optimisation; -- 2.2.4 Multiresponse Optimisation Using Expert System; -- References; -- 3 An Intelligent, Integrated, Problem-Independent Method for Multiresponse Process Optimisation; -- Abstract; -- 3.1 Method Overview: Intelligent System for Multiresponse Robust Process Design (IS-MR-RPD) Model; -- 3.2 Design of Experimental Plan; -- 3.2.1 Taguchi's Experimental Design: Orthogonal Arrays; -- 3.2.2 Expert System for the Design of Experiment (ES_DoE) in IS-MR-RPD Model; --3.2.2.1 Expert System Shell Java DON
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