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Interactive Multiobjective Decision Making Under Uncertainty [Hardback]

(Nagoya City University, Japan)
  • Formāts: Hardback, 295 pages, height x width: 234x156 mm, weight: 544 g
  • Izdošanas datums: 18-Nov-2016
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1498763545
  • ISBN-13: 9781498763547
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  • Cena: 171,76 €
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Interactive Multiobjective Decision Making Under UncertaintyPalielināt
  • Formāts: Hardback, 295 pages, height x width: 234x156 mm, weight: 544 g
  • Izdošanas datums: 18-Nov-2016
  • Izdevniecība: CRC Press Inc
  • ISBN-10: 1498763545
  • ISBN-13: 9781498763547
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781498763547.html
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Recently, many books on multiobjective programming have been published. However, only a few books have been published, in which multiobjective programming under the randomness and the fuzziness are investigated. On the other hand, several books on multilevel programming have been published, in which multiple decision makers are involved in hierarchical decision situations. In this book, we introduce the latest advances in the field of multiobjective programming and multilevel programming under uncertainty. The reader can immediately use proposed methods to solve multiobjective programming and multilevel programming, which are based on linear programming or convex programming technique. Organization of each capter is summarized as follows. In Chapter 2, multiobjective programming problems with random variables are formulated, and the corresponding interactive algorithms are developed to obtain a satisfactory solution, in which the fuzziness of human's subjective judgment for permission levels are considered. In Chapter 3, multiobjective programming problems with fuzzy random variables are formulated, and the corresponding interactive algorithms are developed to obtain a satisfactory solution, in which not only the uncertainty of fuzzy random variables but also the fuzziness of human's subjective judgment for permission levels are considered. In Chapter 4, multiobjective multilevel programming is discussed, and the interactive algorithms are developed to obtain a satisfactory solution, in which the hierarchical decision structure of multiple decision makers is reflected. In Chapter 5, two kinds of farm planning problems are solved by applying the proposed method, in which cost coefficients of crops are expressed by random variables.

Preface vii
1 Introduction
1(8)
1.1 Multiobjective decision making problems (MOPs)
1(2)
1.2 Multiobjective decision making problems (MOPs) under uncertainty
3(2)
1.3 Content organization
5(4)
2 Multiobjective Stochastic Programming Problems (MOSPs)
9(62)
2.1 Fuzzy approaches for MOSPs with a special structure
11(13)
2.1.1 Formulations using probability and fractile models
11(5)
2.1.2 Fuzzy approaches based on linear programming
16(5)
2.1.3 A numerical example
21(3)
2.2 Fuzzy approaches for MOSPs with variance covariance matrices
24(13)
2.2.1 Formulations using probability and fractile models
24(5)
2.2.2 Fuzzy approaches based on convex programming
29(6)
2.2.3 A numerical example
35(2)
2.3 Interactive decision making for MOSPs with a special structure
37(17)
2.3.1 A formulation using a probability model
38(6)
2.3.2 A formulation using a fractile model
44(5)
2.3.3 An interactive linear programming algorithm
49(2)
2.3.4 A numerical example
51(3)
2.4 Interactive decision making for MOSPs with variance covariance matrices
54(17)
2.4.1 A formulation using a probability model
55(7)
2.4.2 A formulation using a fractile model
62(4)
2.4.3 An interactive convex programming algorithm
66(1)
2.4.4 A numerical example
67(4)
3 Multiobjective Fuzzy Random Programming Problems (MOFRPs)
71(66)
3.1 Interactive decision making for MOFRPs with a special structure
73(20)
3.1.1 MOFRPs and a possibility measure
73(2)
3.1.2 A formulation using a probability model
75(6)
3.1.3 A formulation using a fractile model
81(7)
3.1.4 An interactive linear programming algorithm
88(3)
3.1.5 A numerical example
91(2)
3.2 Interactive decision making for MOFRPs with variance covariance matrices
93(20)
3.2.1 MOFRPs and a possibility measure
94(1)
3.2.2 A formulation using a probability model
95(7)
3.2.3 A formulation using a fractile model
102(7)
3.2.4 An interactive convex programming algorithm
109(1)
3.2.5 A numerical example
110(3)
3.3 Interactive decision making for MOFRPs using an expectation variance model
113(13)
3.3.1 MOFRPs and a possibility measure
114(1)
3.3.2 Formulations using an expectation model and a variance model
115(3)
3.3.3 A formulation using an expectation variance model
118(5)
3.3.4 An interactive convex programming algorithm
123(1)
3.3.5 A numerical example
124(2)
3.4 Interactive decision making for MOFRPs with simple recourse
126(11)
3.4.1 An interactive algorithm for MOFRPs with simple recourse
127(6)
3.4.2 An interactive algorithm for MOFRPs with simple recourse using a fractile model
133(4)
4 Hierarchical Multiobjective Programming Problems (HMOPs) Involving Uncertainty Conditions
137(56)
4.1 Hierarchical multiobjective stochastic programming problems (HMOSPs) using a probability model
139(11)
4.1.1 A formulation using a probability model
139(6)
4.1.2 An interactive linear programming algorithm
145(2)
4.1.3 A numerical example
147(3)
4.2 Hierarchical multiobjective stochastic programming problems (HMOSPs) using a fractile model
150(10)
4.2.1 A formulation using a fractile model
150(5)
4.2.2 An interactive linear programming algorithm
155(1)
4.2.3 A numerical example
156(4)
4.3 Hierarchical multiobjective stochastic programming problems (HMOSPs) based on the fuzzy decision
160(19)
4.3.1 A formulation using a probability model
160(7)
4.3.2 A formulation using a fractile model
167(6)
4.3.3 An interactive linear programming algorithm
173(2)
4.3.4 A numerical example
175(4)
4.4 Hierarchical multiobjective fuzzy random programming problems (HMOFRPs) based on the fuzzy decision
179(14)
4.4.1 HMOFRPs and a possibility measure
179(2)
4.4.2 A formulation using a fractile model
181(6)
4.4.3 An interactive linear programming algorithm
187(2)
4.4.4 A numerical example
189(4)
5 Multiobjective Two-Person Zero-Sum Games
193(18)
5.1 Two-person zero-sum games with vector payoffs
194(6)
5.2 Two-person zero-sum games with vector fuzzy payoffs
200(6)
5.3 Numerical examples
206(5)
6 Generalized Multiobjective Programming Problems (GMOPs)
211(34)
6.1 GMOPs with a special structure
212(18)
6.1.1 A formulation using a fractile model and a possibility measure
212(13)
6.1.2 An interactive linear programming algorithm
225(2)
6.1.3 A numerical example
227(3)
6.2 GMOPs with variance covariance matrices
230(15)
6.2.1 A formulation using a fractile model and a possibility measure
230(13)
6.2.2 An interactive convex programming algorithm
243(2)
7 Applications in Farm Planning
245(22)
7.1 A farm planning problem in Japan
246(8)
7.2 A farm planning problem in the Philippines
254(4)
7.3 A farm planning problem with simple recourse in the Philippines
258(4)
7.4 A vegetable shipment problem in Japan
262(5)
References 267(14)
Index 281
Hitoshi Yano received his B.E. and M.E. degrees in systems engineering at Kobe University in 1980 and 1982, respectively, and his D.E. degree from Osaka University in 1988. From 1983 to 1989, he was a Research Associate in the Faculty of Economics at Kagawa University. From 1989 to 1996, he was an Associate Professor at Nagoya Municipal Women's College. From 1996 to 2004, he was an Associate Professor in the School of Humanities and Social Sciences at Nagoya City University. He is currently a Professor in the Graduate School of Humanities and Social Sciences at Nagoya City University. He won Best Paper Awards from the IAENG International Conference on Operations Research in 2011, 2013, and 2014.