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100   $a20230824d2024      u|         0
101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aProbability-Based Multi-objective Optimization for Material Selection /$fby Maosheng Zheng, Jie Yu, Haipeng Teng, Ying Cui, Yi Wang
205   $a2nd ed. 2024.
210  1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024.
215   $a1 online resource (213 pages)
311 08$a9789819939381 
327   $aHistory 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.
330   $aThe 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. .
606   $aMaterials
606   $aMathematical optimization
606   $aChemistry
606   $aProbabilities
606   $aMaterials Engineering
606   $aOptimization
606   $aMaterials Chemistry
606   $aApplied Probability
615  0$aMaterials.
615  0$aMathematical optimization.
615  0$aChemistry.
615  0$aProbabilities.
615 14$aMaterials Engineering.
615 24$aOptimization.
615 24$aMaterials Chemistry.
615 24$aApplied Probability.
676   $a620.11
700   $aZheng$b Maosheng$0905479
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910760254803321
996   $aProbability-Based Multi-Objective Optimization for Material Selection$93598057
997   $aUNINA