04687nam a22005895i 4500 978-981-99-3939-8 DE-He213 20251030110812.0 cr nn 008mamaa 230824s2024 si | s |||| 0|eng d 9789819939398 978-981-99-3939-8 10.1007/978-981-99-3939-8 doi TA401-492 TGM bicssc TEC021000 bisacsh TGM thema Zheng, Maosheng. author. aut http://id.loc.gov/vocabulary/relators/aut 65722 Probability-Based Multi-objective Optimization for Material Selection [electronic resource] / by Maosheng Zheng, Jie Yu, Haipeng Teng, Ying Cui, Yi Wang. 2nd ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIX, 202 p. 25 illus., 3 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda History and Current Status of Material Selection with Multi – objective Optimization -- Introduction to Multi – objective Optimization in Material Selections -- Fundamental Principle of Probability - Based Multi - Objective Optimization and Applications -- Robustness Evaluation with Probability-Based Multi-objective Optimization -- Extension of Probability – based Multi – objective Optimization in Condition of the Utility with Desirable Value -- Hybrids of Probability – Based Multi – Objective Optimization with Experimental Design Methodologies -- Discretization of Simplified Evaluation in Probability-Based Multi-objective Optimization by Means of GLP and Uniform Experimental Design -- Fuzzy- based Probabilistic Multi-objective Optimization -- Cluster Analyses of Multiple Objectives -- Applications of Probability – based Multi – objective Optimization beyond Material Selection -- Treatment of Portfolio Investment by Means of Probability-BasedMulti-objective Optimization -- Treatment of Multi-objective Shortest Path Problem by Means of Probability-Based Multi-objective -- Discussion on preferable probability, discretization, error analysis and hybrid of sequential uniform design with PMOO -- General Conclusions. The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields. . Materials. 65723 Mathematical optimization. 65522 Chemistry. Probabilities. 65724 Materials Engineering. 65725 Optimization. 65528 Materials Chemistry. 65726 Applied Probability. 65727 Yu, Jie. author. aut http://id.loc.gov/vocabulary/relators/aut 65728 Teng, Haipeng. author. aut http://id.loc.gov/vocabulary/relators/aut 65729 Cui, Ying. author. aut http://id.loc.gov/vocabulary/relators/aut 65730 Wang, Yi. author. aut http://id.loc.gov/vocabulary/relators/aut 65731 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819939381 Printed edition: 9789819939404 Printed edition: 9789819939411 https://doi.org/10.1007/978-981-99-3939-8 ZDB-2-ENG ZDB-2-SXE 56245 56245