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Part 1 Constraint Programming and Foundations of Graph Theory |
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1 | (46) |
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3 | (2) |
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Chapter 1 Introduction to Constraint Programming |
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5 | (18) |
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7 | (1) |
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1.2 What is a constraint? |
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8 | (2) |
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1.3 What is a global constraint? |
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10 | (1) |
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1.4 What is a propagation algorithm? |
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11 | (3) |
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1.5 What is a consistency level? |
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14 | (1) |
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1.6 What is a constraint solver? |
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15 | (2) |
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1.7 Constraint solvers at work |
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17 | (4) |
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1.7.1 Importance of modeling |
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17 | (3) |
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1.7.2 Importance of heuristics in guiding research |
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20 | (1) |
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1.7.3 Importance of using global constraints |
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21 | (1) |
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1.8 Organization structure |
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21 | (2) |
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Chapter 2 Graph Theory and Constraint Programming |
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23 | (16) |
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2.1 Modeling graphs with constraint programming |
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24 | (10) |
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2.1.1 Representing a family of graphs |
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24 | (3) |
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2.1.2 Useful graph definitions and properties |
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27 | (6) |
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2.1.3 Graph implementation and complexity |
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33 | (1) |
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2.2 Graph theory at work in constraint programming |
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34 | (3) |
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2.3 Constraint programming at work in graph theory |
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37 | (2) |
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Chapter 3 Tree Graph Partitioning |
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39 | (8) |
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39 | (3) |
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42 | (5) |
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Part 2 Characterization of Tree-Based Graph Partitioning Constraints |
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47 | (146) |
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Chapter 4 Tree Constraints in Undirected Graphs |
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49 | (34) |
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49 | (2) |
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4.2 Definition of constraints |
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51 | (5) |
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4.3 A filtering algorithm for the proper-forest constraint |
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56 | (14) |
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4.3.1 A solution for the proper-forest constraint |
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57 | (2) |
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4.3.2 Hybrid-consistency for the proper-forest constraint |
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59 | (2) |
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4.3.3 Correction and completion |
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61 | (3) |
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64 | (6) |
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4.4 Filtering algorithm for the resource-forest constraint |
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70 | (10) |
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4.4.1 Existence of a solution for the resource-forest constraint |
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70 | (2) |
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4.4.2 Hybrid-consistency for the resource-forest constraint |
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72 | (1) |
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4.4.3 Correction and completion |
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73 | (6) |
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79 | (1) |
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4.5 Summary of undirected tree constraints |
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80 | (3) |
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Chapter 5 Tree Constraints in Directed Graphs |
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83 | (34) |
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83 | (3) |
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5.2 Definition of constraints |
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86 | (3) |
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5.3 Filtering algorithm for the tree constraint |
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89 | (7) |
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5.3.1 Existence of a solution for a tree constraint |
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89 | (2) |
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5.3.2 General arc-consistency for the tree constraint |
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91 | (2) |
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5.3.3 Correction and completion |
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93 | (3) |
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96 | (1) |
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5.4 Filtering algorithm for the proper-tree constraint |
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96 | (17) |
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5.4.1 Limits on the number of proper trees |
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99 | (4) |
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5.4.2 Existence of a solution for the proper-tree constraint |
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103 | (1) |
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5.4.3 Filtering algorithm for the proper-tree constraint |
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104 | (5) |
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109 | (3) |
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112 | (1) |
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5.5 Summary of tree constraints in directed and undirected graphs |
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113 | (4) |
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Chapter 6 Additional Constraints Linked to Graph Partitioning |
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117 | (36) |
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6.1 Definition of restrictions |
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118 | (5) |
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123 | (6) |
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124 | (1) |
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6.2.2 Precedence constraints |
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124 | (2) |
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6.2.3 Conditional precedence constraints |
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126 | (1) |
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6.2.4 Constraints on the interior half-degree of vertices |
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127 | (1) |
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6.2.5 Incomparability constraints |
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128 | (1) |
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6.3 Interaction between the number of trees and the number of proper trees |
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129 | (1) |
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6.4 Relation of precedence between the vertices of the graph |
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130 | (7) |
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6.4.1 Limitations on the maximum number of trees |
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131 | (1) |
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6.4.2 Filtering linked to a set of precedence constraints |
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132 | (2) |
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6.4.3 Filtering and complexity algorithm |
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134 | (3) |
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6.5 Relation of conditional precedence |
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137 | (3) |
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6.5.1 Filtering linked to a conditional precedence set |
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138 | (1) |
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6.5.2 Algorithmic and complexity |
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139 | (1) |
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6.6 Relation of incomparability between graph vertices |
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140 | (3) |
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6.6.1 Filtering linked to incomparability constraints |
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141 | (1) |
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6.6.2 Filtering and complexity algorithm |
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142 | (1) |
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6.7 Interactions between precedence and incomparability constraints |
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143 | (5) |
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6.7.1 Improving filtering via interactions |
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143 | (3) |
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6.7.2 Deduction of new precedence constraints |
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146 | (2) |
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6.8 Constraining the interior half-degree of each vertex |
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148 | (3) |
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151 | (2) |
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Chapter 7 The Case of Disjoint Paths |
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153 | (22) |
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7.1 Minimum number of paths in acyclic directed graphs |
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156 | (5) |
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7.2 Minimum number of paths in any directed graph |
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161 | (8) |
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7.2.1 Estimating the number of paths partitioning a CFC |
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164 | (3) |
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7.2.2 Estimating the number of paths between two CFCs |
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167 | (2) |
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7.3 A path partitioning constraint |
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169 | (4) |
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173 | (2) |
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Chapter 8 Implementation of a Tree Constraint |
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175 | (18) |
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8.1 Original implementation |
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176 | (5) |
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8.1.1 The tree constraint |
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176 | (3) |
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8.1.2 The extended-tree constraint |
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179 | (1) |
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8.1.3 Limitations of the approach: illustration using the tree constraint |
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180 | (1) |
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8.2 Toward a "portable" implementation |
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181 | (10) |
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183 | (2) |
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185 | (1) |
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8.2.2.1 Historic implementation versus adaptable implementation |
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186 | (3) |
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8.2.2.2 Evaluation of portability |
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189 | (2) |
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191 | (2) |
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Part 3 Implementation: Task Planning |
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193 | (32) |
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195 | (4) |
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Chapter 9 First Model in Constraint Programming |
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199 | (6) |
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9.1 Model for the coherence of displacements in space |
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199 | (1) |
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9.2 Modeling resource consumption |
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200 | (1) |
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9.3 Modeling time windows |
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201 | (1) |
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9.4 Modeling coordination constraints between units |
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202 | (1) |
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9.5 Limitations of the proposed model |
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203 | (2) |
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Chapter 10 Advanced Model in Constraint Programming |
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205 | (20) |
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10.1 Modeling the coherence of displacements in space |
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206 | (2) |
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10.2 Modeling resource consumption |
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208 | (1) |
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10.3 Integration of temporal aspects |
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208 | (5) |
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10.4 Propagating time windows |
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213 | (12) |
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10.4.1 Interaction with the graph to be partitioned |
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213 | (2) |
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10.4.2 Interaction with the precedence graph |
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215 | (2) |
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10.4.3 Deriving impossible paths |
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217 | (2) |
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10.4.4 Interaction with the original tree constraint |
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219 | (1) |
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219 | (3) |
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10.4.6 Integration into the mission planning model |
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222 | (3) |
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Part 4 Conclusion and Future Work |
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225 | (8) |
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227 | (4) |
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Chapter 12 Perspectives and Criticisms |
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231 | (2) |
| Bibliography |
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233 | (6) |
| Index |
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239 | |