<p>Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and producti
Optimization of temporal networks under uncertainty
β Scribed by Wiesemann W.
- Publisher
- Springer
- Year
- 2012
- Tongue
- English
- Leaves
- 168
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Table of Contents
Cover......Page 1
Optimization of Temporal Networks under Uncertainty
......Page 4
Preface......Page 6
Contents......Page 8
List of Figures......Page 10
List of Tables......Page 12
1.1 Motivation......Page 13
1.2 Book Outline......Page 18
1.3 Notation......Page 20
2.1 Temporal Networks......Page 21
2.2 Optimization Under Uncertainty......Page 22
2.2.1 Stochastic Programming......Page 23
2.2.2 Robust Optimization......Page 27
2.2.3 Stochastic Dynamic Programming......Page 29
2.3 Optimization of Temporal Networks under Uncertainty......Page 31
3.1 Introduction......Page 34
3.2 Literature Review......Page 35
3.3 Problem Formulation......Page 38
3.4 Solution Procedure......Page 43
3.4.1 Efficient Nodal Bounds......Page 51
3.4.2 Warm-Start Technique......Page 54
3.5 Numerical Results......Page 56
3.5.1 TPT Policies and Alternative Problem Formulations......Page 57
3.5.2 Performance of the Branch-and-Bound Procedure......Page 61
3.6 Conclusion......Page 63
4.1 Introduction......Page 64
4.2 Literature Review......Page 66
4.3 The Service Composition Problem......Page 68
4.4 Mathematical Programming Formulation......Page 69
4.5 Case Study......Page 75
4.6 Scalability......Page 79
4.7 Conclusion......Page 80
5.1 Introduction......Page 82
5.2 Deterministic Resource Allocation......Page 85
5.3 Resource Allocation Under Uncertainty......Page 91
5.4 Numerical Results......Page 105
5.5 Extensions......Page 110
5.5.1 Moment Ambiguity......Page 111
5.5.2 Iterative Path Selection Procedure......Page 112
5.6 Conclusion......Page 114
6.1 Introduction......Page 115
6.2.1 The Robust Resource Allocation Problem......Page 117
6.2.2 Decision Rule Approximations......Page 118
6.2.3 Complexity Analysis......Page 121
6.3 Path-Wise Problem Formulation......Page 124
6.4 Lower Bounds......Page 133
6.5 Upper Bounds......Page 138
6.6 Numerical Results for Random Test Instances......Page 150
6.7 Case Study: VLSI Design......Page 153
6.8 Conclusion......Page 158
References......Page 159
Index......Page 166
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