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Random-Like Bi-level Decision Making 1st ed. 2016 [Pehme köide]

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  • Formaat: Paperback / softback, 401 pages, kõrgus x laius: 235x155 mm, kaal: 6263 g, 60 Illustrations, black and white; XI, 401 p. 60 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 688
  • Ilmumisaeg: 30-Aug-2016
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811017670
  • ISBN-13: 9789811017674
Teised raamatud teemal:
Random-Like Bi-level Decision Making 1st ed. 2016Suurem pilt
  • Formaat: Paperback / softback, 401 pages, kõrgus x laius: 235x155 mm, kaal: 6263 g, 60 Illustrations, black and white; XI, 401 p. 60 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 688
  • Ilmumisaeg: 30-Aug-2016
  • Kirjastus: Springer Verlag, Singapore
  • ISBN-10: 9811017670
  • ISBN-13: 9789811017674
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789811017674.html
Märksõnad:
Among the various multi-level formulations of mathematical models in decision making processes, this book focuses on the bi-level model. Being the most frequently used, the bi-level model addresses conflicts which exist in multi-level decision making processes. From the perspective of bi-level structure and uncertainty, this book takes real-life problems as the background, focuses on the so-called random-like uncertainty, and develops the general framework of random-like bi-level decision making problems. The random-like uncertainty considered in this book includes random phenomenon, random-overlapped random (Ra-Ra) phenomenon and fuzzy-overlapped random (Ra-Fu) phenomenon. Basic theory, models, algorithms and practical applications for different types of random-like bi-level decision making problems are also presented in this book.




1 Foundations of Random-Like Bi-Level Decision Making
1(76)
1.1 Random Sets Theory
2(43)
1.1.1 General Concepts
2(13)
1.1.2 Random Variable
15(8)
1.1.3 Fuzzy Variable
23(4)
1.1.4 Ra-Ra Variable
27(10)
1.1.5 Ra-Fu Variable
37(8)
1.2 Bi-Level Programming
45(19)
1.2.1 Linear Bi-Level Programming
47(2)
1.2.2 Nonlinear Bi-Level Programming
49(3)
1.2.3 Complexity and Optimality Conditions
52(1)
1.2.4 Traditional Methods
53(7)
1.2.5 Developments of Algorithms
60(4)
1.3 Synthetical Analysis of Literature
64(13)
References
70(7)
2 Bi-Level Decision Making in Random Phenomenon
77(122)
2.1 Regional Water Resources Allocation Problem
77(5)
2.1.1 Random Phenomenon
80(1)
2.1.2 Bi-Level Description
81(1)
2.2 Bi-Level Decision Making Models with Random Coefficients
82(12)
2.3 Random EEEE Model
94(22)
2.3.1 General Form of Random EEEE Model
95(3)
2.3.2 Reference Point Method for Linear Models
98(10)
2.3.3 Genetic Approach for Nonlinear Models
108(8)
2.4 Random CECC Model
116(42)
2.4.1 General Form of Random CECC Model
116(5)
2.4.2 KKT Method for Linear Model
121(29)
2.4.3 Genetic Algorithm for Nonlinear Model
150(8)
2.5 Random DEDC Model
158(19)
2.5.1 General Form of Random DEDC Model
158(2)
2.5.2 Interactive Fuzzy Goal Programming for Linear Model
160(8)
2.5.3 Random Simulation-Based BPMOGA for Nonlinear Model
168(9)
2.6 Regional Water Resources Allocation Problem in the Gan-Fu Plain
177(22)
2.6.1 Modelling
177(5)
2.6.2 Presentation of the Case Problem
182(3)
2.6.3 Parameter Selection for EBS-Based GA
185(1)
2.6.4 Results and Discussion
186(6)
References
192(7)
3 Bi-Level Decision Making in Ra-Ra Phenomenon
199(84)
3.1 Transport Flow Distribution Problem
199(4)
3.1.1 Ra-Ra Phenomenon
201(1)
3.1.2 Bi-Level Description
202(1)
3.2 Bi-Level Decision Making Model with Ra-Ra Coefficients
203(7)
3.3 Ra-Ra EEDE Model
210(16)
3.3.1 General Form of Ra-Ra EEDE Model
210(2)
3.3.2 The Steepest Descent Direction Method for Linear Model
212(7)
3.3.3 Ra-Ra Simulation-Based PSO for Nonlinear Models
219(7)
3.4 Ra-Ra ECEC Model
226(20)
3.4.1 General Form of Ra-Ra ECEC Model
226(2)
3.4.2 A Homotopy Method for Linear Model
228(9)
3.4.3 Ra-Ra Simulation-Based CHK-PSO for Nonlinear Models
237(9)
3.5 Ra-Ra DCCC Model
246(18)
3.5.1 General Form of Ra-Ra DCCC Model
246(3)
3.5.2 Interactive Balance Space Approach for Linear Model
249(9)
3.5.3 Ra-Ra Simulation-Based CST-PSO for Nonlinear Models
258(6)
3.6 Transport Flow Distribution of the SBY Hydropower Project
264(19)
3.6.1 Modelling
264(6)
3.6.2 Presentation of the SBY Hydropower Project
270(6)
3.6.3 Solutions and Discussion
276(4)
References
280(3)
4 Bi-Level Decision Making in Ra-Fu Phenomenon
283(82)
4.1 Construction Site Security Planning Problem
283(5)
4.1.1 Ra-Fu Phenomena
286(1)
4.1.2 Bi-Level Description
286(2)
4.2 Bi-Level Decision Making Model with Ra-Fu Coefficients
288(1)
4.3 Ra-Fu EECC Model
289(27)
4.3.1 General Form of Ra-Fu EECC Model
289(1)
4.3.2 Constrained Variable Metric Method for Linear Model
290(17)
4.3.3 Ra-Fu Simulation-Based PGSA for Nonlinear Model
307(9)
4.4 Ra-Fu CCDD Model
316(16)
4.4.1 General Form of Ra-Fu CCDD Model
316(1)
4.4.2 Fuzzy Decision Method for Linear Model
317(9)
4.4.3 Ra-Fu Simulation-Based PGSA-GA for Nonlinear Model
326(6)
4.5 Ra-Fu DDEE Model
332(11)
4.5.1 General Form of Ra-Fu DDEE Model
332(1)
4.5.2 Interactive Method for Linear Model
333(4)
4.5.3 Ra-Fu Simulation-Based PGSA-PSO for Nonlinear Model
337(6)
4.6 Security Planning of the LT Hydropower Construction Project
343(22)
4.6.1 Modelling
343(7)
4.6.2 Solving Procedure by PGSA Algorithm
350(6)
4.6.3 Data Collection
356(1)
4.6.4 Solution and Discussion
356(6)
References
362(3)
5 Methodology From an Equilibria Viewpoints
365(22)
5.1 Motivation for Equilibrium
365(3)
5.2 Equilibria in Real-World Problems
368(4)
5.3 Model System in Equilibria
372(4)
5.4 Equilibria Algorithms
376(5)
5.5 Perspectives
381(6)
5.5.1 Models
382(1)
5.5.2 Theories
383(1)
5.5.3 Algorithms
384(1)
5.5.4 Applications
384(1)
References
385(2)
Appendix MATLAB Codes
387(12)
A.1 Matlab® File for Example 2.3
387(1)
A.2 Matlab® File for Example 2.7
387(1)
A.3 Matlab® File for Example 2.10
388(1)
A.4 Matlab® File for Example 3.2
388(1)
A.5 Matlab® File for Example 3.3
388(1)
A.6 Matlab® File for Example 3.6
389(1)
A.7 Matlab® File for Example 4.2
389(3)
A.8 Matlab® File for Example 4.3
392(3)
A.9 Matlab® File for Example 4.6
395(4)
Index 399
Jiuping Xu holds a Ph.D. in Applied Mathematics from Tsinghua University and a Ph.D. in Physical Chemistry from Sichuan University, where he became a professor in 1995. At present, he is the Associate Vice President of the Business School at Sichuan University, Chengdu, China, the Dean of the Business School at Sichuan University, Chengdu, China, a lifetime Academician of the International Academy for Systems and Cybernetic Sciences, an Academician of Mongolian National Academy of Sciences, an Academician of Lotfi Zadeh International Academy of Sciences, President of the International Society for Management Science and Engineering Management and General Chair of the International Conference on Management Science and Engineering Management. He has published more than 40 books in over 10 publishing houses, and over 600 papers in more than 150 international journals. Zongmin Li obtained her Ph.D. in Management Science and Engineering from Sichuan University.Currently she is an assistant professor in Business School of Sichuan University, Chengdu, China. She was a visiting scholar in Drexel University, LeBow College of Business in 2012.9-2013.9. She is an active scholar in the areas of mathematical modeling, uncertain decision making, algorithm, multi-criteria decision analysis and applications. Her research outputs appear on some international journals such as Omega, Automation in Construction, Knowledge-Based Systems as well as some international conference proceedings. Zhimiao Tao holds a Ph.D. in Management Science and Engineering from Sichuan University. Currently Dr. Tao is an associate professor in Business School of  Sichuan University, Chengdu, China, and a visiting scholar in University of Washington, Seattle. He has published a book Rough Multiple Objective Decision Making in Taylor & Francis Press and many papers on international journals, such as Information Sciences, Mathematics and Computers in Simulation, Petroleum Science and Technology respectively. His research interests include rough set theory, bi-level programming and intelligent algorithm.