Probability-Based Multi-objective Optimization for Material Selection

✍ Scribed by Maosheng Zheng, Jie Yu, Haipeng Teng, Ying Cui, Yi Wang

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 214
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface to the Second Edition
Preface to the First Edition
Contents
About the Authors
1 History and Current Status of Material Selection with Multi-objective Optimization
1.1 Brief Introduction
1.2 Evolution of Material Selections
1.3 Evolution of Multi-objective Optimization
1.4 Summary and Conclusions
References
2 Introduction to Multi-objective Optimization in Material Selections
2.1 Introduction
2.2 Previous Approaches for Multi-objective Optimization of Material Selection
2.2.1 Qualitative Approach
2.2.2 Quantitative Approach
2.2.3 Discussion and Summary of the Previous Approaches for Multi-objective Optimization of Material Selection
2.3 Fundamental Consideration of Multiple objective Optimization for Material Selection
2.3.1 Statement of Situation
2.3.2 Basic Principles for Selection of Equipment Materials
2.3.3 Basic Procedure for Material Selection
2.4 Conclusion
References
3 Fundamental Principle of Probability-Based Multi-objective Optimization and Applications
3.1 Introduction
3.2 Multi-objective Optimization in Viewpoint of System Theory
3.3 Arithmetic of Probability Treatment
3.4 Quantitative Approach for Material Selection in Respect to Probability Theory
3.4.1 Concept of Preferable Probability
3.4.2 Probability-Based Approach
3.5 Applications of the Probability-Based Method for Multi-objective Optimization in Material Selection
3.6 Other Applications in More Broader and General Issues
3.7 Concluding Remarks
References
4 Robustness Evaluation with Probability-Based Multi-objective Optimization
4.1 Introduction
4.2 Extension of Probability-Based Multi-objective Optimization to Contain Robustness
4.3 Application of the Extended PMOO in Evaluation of Optimal Problems with Variance of Data in Material Engineering
4.4 Conclusion
References
5 Extension of Probability-Based Multi-objective Optimization in Condition of the Utility with Desirable Value
5.1 Introduction
5.2 Assessments of Partial and Overall Preferable Probability for Performance Response with Desirable Value in the Probability-Based Multiple Objectives Optimization
5.2.1 One Range Desirable Value Problem
5.2.2 One Side Desirable Value Problem
5.3 Applications
5.4 Concluding Remarks
References
6 Hybrids of Probability-Based Multi-objective Optimization with Experimental Design Methodologies
6.1 Introduction
6.2 Hybrid of Probability-Based Multi-objective Optimization with Orthogonal Experimental Design
6.2.1 Algorithm of the Hybrid for PMOO with Orthogonal Experimental Design
6.2.2 Application of the Hybrid of PMOO with Orthogonal Experimental Design in Material Selection
6.3 Hybrid of Probability-Based Multi-objective Optimization with Response Surface Methodology Design
6.3.1 Algorithm of the Hybrid for PMOO with Response Surface Methodology (RSM)
6.3.2 Application of the Hybrid of PMOO with Response Surface Methodology Design in Material Selection
6.4 Hybrid of Probability-Based Multi-objective Optimization with Uniform Experimental Design Methodology
6.4.1 Algorithm of the Hybrid for PMOO with Uniform Experimental Design Methodology (UED)
6.4.2 Application of the Hybrid of PMOO with Uniform Experimental Design Methodology in Material Selection
6.5 Conclusion
References
7 Discretization of Simplified Evaluation in Probability-Based Multi-objective Optimization by Means of GLP and Uniform Experimental Design
7.1 Introduction
7.2 Fundamental Characteristics of Uniform Experimental Design
7.2.1 Main Features of Uniform Experimental Design
7.2.2 Fundamental Principle of Uniform Experimental Design
7.3 Feature Analysis of the Periodic Function in a Single Period
7.4 Typical Examples for the Efficient Approach of Numerical Integration for a Single Peak Function Based on Rules of GLD and Uniform Design Method
7.5 Typical Examples of Applications of the Finite Sampling Point Method in Assessment of Probability-Based Multi-objective Optimization
7.6 Conclusive Remarks
References
8 Fuzzy-Based Probabilistic Multi-objective Optimization for Material Selection
8.1 Introduction
8.2 Formulation of Fuzzy Probability-Based Multi-objective Optimization (FPMOO)
8.2.1 Membership Value of Material Performance in Fuzzy Language
8.2.2 Fuzzy Probability-Based Multi-objective Optimization (FPMOO)
8.3 Illustrative Example
8.4 Concluding Remarks
References
9 Cluster Analysis of Separation of “Independent Objective” for Probability-Based Multi-objective Optimization
9.1 Introduction
9.2 Characterization of Similarity Between Performances or Samples
9.3 Application of Clustering Analysis in Separation of “Independent Objective” for Multi-objective Optimization
9.4 Conclusion
References
10 Applications of Probability-Based Multi-objective Optimization Beyond Material Selection
10.1 Introduction
10.2 Application of the Multi-objective Optimization in Drug Design and Extraction
10.2.1 Optimal Preparation of Encapsulation Composite of Water-Soluble Chitosan/Poly-Gamma-Glutamic Acid-Tanshinone IIA with Response Surface Methodology Design
10.2.2 Optimal Preparation of Glycerosomes–Triptolide as an Encapsulation Composite with Orthogonal Experimental Design
10.2.3 Optimization of Compatibility of the Traditional Chinese Medicine Drug by Using Orthogonal Experimental Design
10.2.4 Optimization of Multi-objective Drug Extraction Conditions Based on Uniform Experimental Designs
10.3 Application of the Probability-Based Multi-objective Optimization in Military Engineering Project with Weighting Factor
10.3.1 Decision Making of Multi-objective Military Engineering Investment
10.3.2 Flexible Ability Assessment of Antiaircraft Weapon System
10.4 Comparative Analysis of Scheme Selection for Water Purification Treatment by Using PMOO with the Traditional MCDM
10.5 Application of the Probability-Based Multi-objective Optimization in Power Equipment
10.6 Conclusion
References
11 Treatment of Portfolio Investment by Means of Probability-Based Multi-objective Optimization
11.1 Introduction
11.2 Solution of Portfolio Problem by Means of Probability-Based Multi-objective Optimization
11.3 Example of Case with Four Securities
11.4 Conclusion
References
12 Treatment of Multi-objective Shortest Path Problem by Means of Probability-Based Multi-objective Optimization
12.1 Introduction
12.2 Approach for Multi-objective Shortest Path Problem Based on Probability Theory
12.2.1 Probabilistic Model of Multi-objective Optimization Problem
12.2.2 Assessment Procedure of Simultaneous Optimization of Multi-objective Shortest Path Problem in Respect of Probability Theory
12.3 Application of the Probability-Based Approach of Multi-objective Shortest Path Problem
12.3.1 Application in Hazardous Materials Transportation Path Problem
12.3.2 Application in Multi-objective Inter-Model Transportation of Grain from Northern China to the South Considering Weather Factor
12.4 Conclusion
References
13 Discussion on Preferable Probability, Discretization, Error Analysis, and Hybrid of Sequential Uniform Design with PMOO
13.1 On Preferable Probability
13.2 On the Assessments of Robustness of Performance Utility with Uncertainty
13.3 On the Number of Discretized Sampling Points of Evaluation in Probability-Based Multi-objective Optimization by Means of GLP and Uniform Experimental Design
13.4 Error Analysis
13.5 Hybrid of Sequential Uniform Design with Probability-Based Multi-objective Optimization
13.6 On Weighting Factor
13.7 Conclusion
References
14 General Conclusions
Correction to: Probability-Based Multi-objective Optimization for Material Selection
Correction to: M. Zheng et al., Probability-Based Multi-objective Optimization for Material Selection, https://doi.org/10.1007/978-981-99-3939-8