Theory and Practice of Uncertain Programming

By: Baoding Liu

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Hardcover | 18 March 2009 | Edition Number 2

At a Glance

Hardcover
216 Pages

Edition Type
Revised

Dimensions(cm)
23.39 x 15.6 x 1.42

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Real-life decisions are usually made in the state of uncertainty such as randomness and fuzziness. How do we model optimization problems in uncertain environments? How do we solve these models? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertain programming theory, including numerous modeling ideas, hybrid intelligent algorithms, and applications in system reliability design, project scheduling problem, vehicle routing problem, facility location problem, and machine scheduling problem. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

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Mathematical Programming	p. 1
Single-Objective Programming	p. 1
Multiobjective Programming	p. 3
Goal Programming	p. 4
Dynamic Programming	p. 6
Multilevel Programming	p. 7
Genetic Algorithms	p. 9
Representation Structure	p. 10
Handling Constraints	p. 10
Initialization Process	p. 10
Evaluation Function	p. 11
Selection Process	p. 12
Crossover Operation	p. 12
Mutation Operation	p. 13
General Procedure	p. 13
Numerical Experiments	p. 14
Neural Networks	p. 19
Basic Concepts	p. 19
Function Approximation	p. 21
Neuron Number Determination	p. 21
Backpropagation Algorithm	p. 22
Numerical Experiments	p. 23
Stochastic Programming	p. 25
Random Variables	p. 25
Expected Value Model	p. 35
Chance-Constrained Programming	p. 37
Dependent-Chance Programming	p. 42
Hybrid Intelligent Algorithm	p. 50
Numerical Experiments	p. 54
Fuzzy Programming	p. 57
Fuzzy Variables	p. 57
Expected Value Model	p. 68
Chance-Constrained Programming	p. 70
Dependent-Chance Programming	p. 74
Hybrid Intelligent Algorithm	p. 76
Numerical Experiments	p. 79
Hybrid Programming	p. 83
Hybrid Variables	p. 83
Expected Value Model	p. 98
Chance-Constrained Programming	p. 99
Dependent-Chance Programming	p. 102
Hybrid Intelligent Algorithm	p. 104
Numerical Experiments	p. 107
Uncertain Programming	p. 111
Uncertain Variables	p. 111
Expected Value Model	p. 118
Chance-Constrained Programming	p. 119
Dependent-Chance Programming	p. 121
Uncertain Dynamic Programming	p. 122
Uncertain Multilevel Programming	p. 124
¿ Graph of Uncertain Programming	p. 127
System Reliability Design	p. 129
Problem Description	p. 129
Stochastic Models	p. 130
Fuzzy Models	p. 134
Hybrid Models	p. 136
Exercises	p. 137
Project Scheduling Problem	p. 139
Problem Description	p. 139
Stochastic Models	p. 140
Fuzzy Models	p. 143
Hybrid Models	p. 145
Exercises	p. 146
Vehicle Routing Problem	p. 147
Problem Description	p. 147
Stochastic Models	p. 149
Fuzzy Models	p. 153
Hybrid Models	p. 154
Exercises	p. 155
Facility Location Problem	p. 157
Problem Description	p. 157
Stochastic Models	p. 157
Fuzzy Models	p. 160
Hybrid Models	p. 163
Exercises	p. 165
Machine Scheduling Problem	p. 167
Problem Description	p. 167
Stochastic Models	p. 168
Fuzzy Models	p. 172
Hybrid Models	p. 175
Exercises	p. 177
References	p. 179
List of Acronyms	p. 197
List of Frequently Used Symbols	p. 199
Index	p. 201
Table of Contents provided by Ingram. All Rights Reserved.

Theory and Practice of Uncertain Programming

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Engineering in General

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6th Edition

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Transforming our shared systems for a changing world

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