Theory and Practice of Uncertain Programming

✍ Scribed by Boading Liu

• Publisher: Physica-Verlag Heidelberg
• Year: 2003
• Tongue: English
• Leaves: 190
• Edition: 1
• Category: Library

✦ Synopsis

Real-life decisions are usually made in the state of uncertainty (randomness, fuzziness, roughness, etc.). 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. It includes numerous modeling ideas, hybrid intelligent algorithms, and various applications in transportation problem, inventory system, facility location & allocation, capital budgeting, topological optimization, vehicle routing problem, redundancy optimization, and scheduling. Researchers, practitioners and students in operations research, management science, information science, system science, and engineering will find this work a stimulating and useful reference.

✦ Table of Contents

Preface......Page 3
Single-Objective Programming......Page 9
Multiobjective Programming......Page 11
Goal Programming......Page 13
Dynamic Programming......Page 14
Multilevel Programming......Page 15
Genetic Algorithms......Page 17
Initialization Process......Page 18
Evaluation Function......Page 19
Crossover Operation......Page 20
General Procedure......Page 21
Numerical Experiments......Page 22
Basic Concepts......Page 27
Neuron Number Determination......Page 29
Backpropagation Algorithm......Page 30
Numerical Experiments......Page 31
Random Variables......Page 33
Expected Value Model......Page 38
Chance-Constrained Programming......Page 40
Dependent-Chance Programming......Page 46
Hybrid Intelligent Algorithm......Page 53
Numerical Experiments......Page 56
Fuzzy Variables......Page 61
Expected Value Model......Page 68
Chance-Constrained Programming......Page 69
Dependent-Chance Programming......Page 73
Hybrid Intelligent Algorithm......Page 76
Numerical Experiments......Page 78
Hybrid Programming......Page 83
Hybrid Variables......Page 84
Expected Value Model......Page 92
Chance-Constrained Programming......Page 93
Dependent-Chance Programming......Page 95
Hybrid Intelligent Algorithm......Page 97
Numerical Experiments......Page 100
Problem Description......Page 105
Stochastic Models......Page 106
Fuzzy Models......Page 110
Hybrid Models......Page 111
Problem Description......Page 115
Stochastic Models......Page 116
Fuzzy Models......Page 119
Hybrid Models......Page 121
Vehicle Routing Problem......Page 123
Problem Description......Page 124
Stochastic Models......Page 125
Fuzzy Models......Page 129
Hybrid Models......Page 131
Problem Description......Page 133
Stochastic Models......Page 134
Fuzzy Models......Page 137
Hybrid Models......Page 139
Problem Description......Page 141
Stochastic Models......Page 142
Fuzzy Models......Page 146
Hybrid Models......Page 149
Uncertain Variables......Page 153
Expected Value Model......Page 155
Chance-Constrained Programming......Page 156
Dependent-Chance Programming......Page 159
Uncertain Dynamic Programming......Page 160
Uncertain Multilevel Programming......Page 161
Graph of Uncertain Programming......Page 165
Index......Page 167