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403 lines
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<span id="sfml">Google OR-Tools 7.4</span>
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<li><a href="../cpp_algorithms/annotated.html">Algorithms</a></li>
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<li><a href="../cpp_sat/annotated.html">CP-SAT</a></li>
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<li><a href="../cpp_graph/annotated.html">Graph</a></li>
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<li><a href="../cpp_routing/annotated.html">Routing</a></li>
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<h1 style="color: #145A32;">C++ Reference: Routing</h1>
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<div class="title">routing_lp_scheduling.h</div> </div>
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<a href="routing__lp__scheduling_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno"> 1</span> <span class="comment">// Copyright 2010-2018 Google LLC</span></div>
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<div class="line"><a name="l00002"></a><span class="lineno"> 2</span> <span class="comment">// Licensed under the Apache License, Version 2.0 (the "License");</span></div>
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<div class="line"><a name="l00003"></a><span class="lineno"> 3</span> <span class="comment">// you may not use this file except in compliance with the License.</span></div>
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<div class="line"><a name="l00004"></a><span class="lineno"> 4</span> <span class="comment">// You may obtain a copy of the License at</span></div>
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<div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment">//</span></div>
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<div class="line"><a name="l00006"></a><span class="lineno"> 6</span> <span class="comment">// http://www.apache.org/licenses/LICENSE-2.0</span></div>
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<div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">//</span></div>
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<div class="line"><a name="l00008"></a><span class="lineno"> 8</span> <span class="comment">// Unless required by applicable law or agreed to in writing, software</span></div>
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<div class="line"><a name="l00009"></a><span class="lineno"> 9</span> <span class="comment">// distributed under the License is distributed on an "AS IS" BASIS,</span></div>
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<div class="line"><a name="l00010"></a><span class="lineno"> 10</span> <span class="comment">// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span></div>
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<div class="line"><a name="l00011"></a><span class="lineno"> 11</span> <span class="comment">// See the License for the specific language governing permissions and</span></div>
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<div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// limitations under the License.</span></div>
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<div class="line"><a name="l00013"></a><span class="lineno"> 13</span>  </div>
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<div class="line"><a name="l00014"></a><span class="lineno"> 14</span> <span class="preprocessor">#ifndef OR_TOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_</span></div>
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<div class="line"><a name="l00015"></a><span class="lineno"> 15</span> <span class="preprocessor">#define OR_TOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_</span></div>
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<div class="line"><a name="l00016"></a><span class="lineno"> 16</span>  </div>
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<div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "absl/memory/memory.h"</span></div>
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<div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "<a class="code" href="routing_8h.html">ortools/constraint_solver/routing.h</a>"</span></div>
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<div class="line"><a name="l00019"></a><span class="lineno"> 19</span> <span class="preprocessor">#include "ortools/glop/lp_solver.h"</span></div>
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<div class="line"><a name="l00020"></a><span class="lineno"> 20</span>  </div>
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<div class="line"><a name="l00021"></a><span class="lineno"> 21</span> <span class="keyword">namespace </span><a class="code" href="namespaceoperations__research.html">operations_research</a> {</div>
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<div class="line"><a name="l00022"></a><span class="lineno"> 22</span>  </div>
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<div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// Classes to solve dimension cumul placement (aka scheduling) problems using</span></div>
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<div class="line"><a name="l00024"></a><span class="lineno"> 24</span> <span class="comment">// linear programming.</span></div>
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<div class="line"><a name="l00025"></a><span class="lineno"> 25</span>  </div>
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<div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment">// Utility class used in the core optimizer to tighten the cumul bounds as much</span></div>
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<div class="line"><a name="l00027"></a><span class="lineno"> 27</span> <span class="comment">// as possible based on the model precedences.</span></div>
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<div class="line"><a name="l00028"></a><span class="lineno"><a class="line" href="classoperations__research_1_1CumulBoundsPropagator.html"> 28</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html">CumulBoundsPropagator</a> {</div>
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<div class="line"><a name="l00029"></a><span class="lineno"> 29</span>  <span class="keyword">public</span>:</div>
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<div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#aff8f29b2fce9f6447474ee6077af1b72">CumulBoundsPropagator</a>(<span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#a72be8a813404822957fbd808d0405226">dimension</a>);</div>
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<div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  </div>
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<div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <span class="comment">// Tightens the cumul bounds starting from the current cumul var min/max,</span></div>
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<div class="line"><a name="l00033"></a><span class="lineno"> 33</span>  <span class="comment">// and propagating the precedences resulting from the next_accessor, and the</span></div>
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<div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="comment">// dimension's precedence rules.</span></div>
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<div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// Returns false iff the precedences are infeasible with the given routes.</span></div>
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<div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// Otherwise, the user can call CumulMin() and CumulMax() to retrieve the new</span></div>
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<div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// bounds of an index.</span></div>
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<div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#a54688e4af975568b0bce87cdf9f26781">PropagateCumulBounds</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  int64 cumul_offset);</div>
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<div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  </div>
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<div class="line"><a name="l00041"></a><span class="lineno"><a class="line" href="classoperations__research_1_1CumulBoundsPropagator.html#a921806c74f3dd622afc937929b69d627"> 41</a></span>  int64 <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#a921806c74f3dd622afc937929b69d627">CumulMin</a>(<span class="keywordtype">int</span> index)<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  <span class="keywordflow">return</span> propagated_bounds_[PositiveNode(index)];</div>
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<div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  }</div>
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<div class="line"><a name="l00044"></a><span class="lineno"> 44</span>  </div>
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<div class="line"><a name="l00045"></a><span class="lineno"><a class="line" href="classoperations__research_1_1CumulBoundsPropagator.html#a3b5250c6480d24af0c95d1f68a7556b7"> 45</a></span>  int64 <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#a3b5250c6480d24af0c95d1f68a7556b7">CumulMax</a>(<span class="keywordtype">int</span> index)<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  <span class="keyword">const</span> int64 negated_upper_bound = propagated_bounds_[NegativeNode(index)];</div>
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<div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  <span class="keywordflow">return</span> negated_upper_bound == kint64min ? kint64max : -negated_upper_bound;</div>
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<div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  }</div>
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<div class="line"><a name="l00049"></a><span class="lineno"> 49</span>  </div>
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<div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classoperations__research_1_1CumulBoundsPropagator.html#a72be8a813404822957fbd808d0405226"> 50</a></span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>& <a class="code" href="classoperations__research_1_1CumulBoundsPropagator.html#a72be8a813404822957fbd808d0405226">dimension</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> dimension_; }</div>
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<div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  </div>
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<div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  <span class="keyword">private</span>:</div>
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<div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  <span class="comment">// An arc "tail --offset--> head" represents the relation</span></div>
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<div class="line"><a name="l00054"></a><span class="lineno"> 54</span>  <span class="comment">// tail + offset <= head.</span></div>
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<div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="comment">// As arcs are stored by tail, we don't store it in the struct.</span></div>
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<div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keyword">struct </span>ArcInfo {</div>
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<div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  <span class="keywordtype">int</span> head;</div>
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<div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  int64 offset;</div>
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<div class="line"><a name="l00059"></a><span class="lineno"> 59</span>  };</div>
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<div class="line"><a name="l00060"></a><span class="lineno"> 60</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">int</span> kNoParent;</div>
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<div class="line"><a name="l00061"></a><span class="lineno"> 61</span>  <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">int</span> kParentToBePropagated;</div>
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<div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  </div>
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<div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// Return the node corresponding to the lower bound of the cumul of index and</span></div>
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<div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// -index respectively.</span></div>
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<div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">int</span> PositiveNode(<span class="keywordtype">int</span> index)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 2 * index; }</div>
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<div class="line"><a name="l00066"></a><span class="lineno"> 66</span>  <span class="keywordtype">int</span> NegativeNode(<span class="keywordtype">int</span> index)<span class="keyword"> const </span>{ <span class="keywordflow">return</span> 2 * index + 1; }</div>
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<div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  </div>
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<div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="keywordtype">void</span> AddNodeToQueue(<span class="keywordtype">int</span> node) {</div>
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<div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="keywordflow">if</span> (!node_in_queue_[node]) {</div>
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<div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  bf_queue_.push_back(node);</div>
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<div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  node_in_queue_[node] = <span class="keyword">true</span>;</div>
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<div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  }</div>
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<div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  }</div>
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<div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  </div>
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<div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  <span class="comment">// Adds the relation first_index + offset <= second_index, by adding arcs</span></div>
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<div class="line"><a name="l00076"></a><span class="lineno"> 76</span>  <span class="comment">// first_index --offset--> second_index and</span></div>
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<div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// -second_index --offset--> -first_index.</span></div>
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<div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="keywordtype">void</span> AddArcs(<span class="keywordtype">int</span> first_index, <span class="keywordtype">int</span> second_index, int64 offset);</div>
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<div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  </div>
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<div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="keywordtype">bool</span> InitializeArcsAndBounds(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  int64 cumul_offset);</div>
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<div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  </div>
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<div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">bool</span> UpdateCurrentLowerBoundOfNode(<span class="keywordtype">int</span> node, int64 new_lb);</div>
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<div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  </div>
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<div class="line"><a name="l00085"></a><span class="lineno"> 85</span>  <span class="keywordtype">bool</span> DisassembleSubtree(<span class="keywordtype">int</span> source, <span class="keywordtype">int</span> target);</div>
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<div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  </div>
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<div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  <span class="keywordtype">bool</span> CleanupAndReturnFalse() {</div>
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<div class="line"><a name="l00088"></a><span class="lineno"> 88</span>  <span class="comment">// We clean-up node_in_queue_ for future calls, and return false.</span></div>
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<div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> node_to_cleanup : bf_queue_) {</div>
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<div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  node_in_queue_[node_to_cleanup] = <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  }</div>
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<div class="line"><a name="l00092"></a><span class="lineno"> 92</span>  bf_queue_.clear();</div>
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<div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
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<div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  }</div>
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<div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  </div>
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<div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="keyword">const</span> RoutingDimension& dimension_;</div>
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<div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="keyword">const</span> int64 num_nodes_;</div>
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<div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  </div>
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<div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="comment">// TODO(user): Investigate if all arcs for a given tail can be created</span></div>
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<div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  <span class="comment">// at the same time, in which case outgoing_arcs_ could point to an absl::Span</span></div>
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<div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  <span class="comment">// for each tail index.</span></div>
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<div class="line"><a name="l00102"></a><span class="lineno"> 102</span>  std::vector<std::vector<ArcInfo>> outgoing_arcs_;</div>
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<div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  </div>
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<div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::deque<int> bf_queue_;</div>
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<div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::vector<bool> node_in_queue_;</div>
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<div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  std::vector<int> tree_parent_node_of_;</div>
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<div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  <span class="comment">// After calling PropagateCumulBounds(), for each node index n,</span></div>
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<div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  <span class="comment">// propagated_bounds_[2*n] and -propagated_bounds_[2*n+1] respectively contain</span></div>
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<div class="line"><a name="l00109"></a><span class="lineno"> 109</span>  <span class="comment">// the propagated lower and upper bounds of n's cumul variable.</span></div>
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<div class="line"><a name="l00110"></a><span class="lineno"> 110</span>  std::vector<int64> propagated_bounds_;</div>
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<div class="line"><a name="l00111"></a><span class="lineno"> 111</span>  </div>
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<div class="line"><a name="l00112"></a><span class="lineno"> 112</span>  <span class="comment">// Vector used in DisassembleSubtree() to avoid memory reallocation.</span></div>
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<div class="line"><a name="l00113"></a><span class="lineno"> 113</span>  std::vector<int> tmp_dfs_stack_;</div>
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<div class="line"><a name="l00114"></a><span class="lineno"> 114</span>  </div>
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<div class="line"><a name="l00115"></a><span class="lineno"> 115</span>  <span class="comment">// Used to store the pickup/delivery pairs encountered on the routes.</span></div>
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<div class="line"><a name="l00116"></a><span class="lineno"> 116</span>  std::vector<std::pair<int64, int64>></div>
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<div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  visited_pickup_delivery_indices_for_pair_;</div>
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<div class="line"><a name="l00118"></a><span class="lineno"> 118</span> };</div>
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<div class="line"><a name="l00119"></a><span class="lineno"> 119</span>  </div>
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<div class="line"><a name="l00120"></a><span class="lineno"> 120</span> <span class="comment">// Utility class used in Local/GlobalDimensionCumulOptimizer to set the LP</span></div>
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<div class="line"><a name="l00121"></a><span class="lineno"> 121</span> <span class="comment">// constraints and solve the problem.</span></div>
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<div class="line"><a name="l00122"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html"> 122</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> {</div>
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<div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="keyword">public</span>:</div>
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<div class="line"><a name="l00124"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a5967f72ac911a2ed971a1ce103d1c47b"> 124</a></span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a5967f72ac911a2ed971a1ce103d1c47b">DimensionCumulOptimizerCore</a>(<span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>,</div>
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<div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keywordtype">bool</span> use_precedence_propagator)</div>
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<div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  : dimension_(<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>),</div>
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<div class="line"><a name="l00127"></a><span class="lineno"> 127</span>  visited_pickup_delivery_indices_for_pair_(</div>
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<div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>->model()->GetPickupAndDeliveryPairs().size(), {-1, -1}) {</div>
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<div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="keywordflow">if</span> (use_precedence_propagator) {</div>
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<div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  propagator_ = absl::make_unique<CumulBoundsPropagator>(<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>);</div>
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<div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  }</div>
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<div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  }</div>
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<div class="line"><a name="l00133"></a><span class="lineno"> 133</span>  </div>
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<div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// In the OptimizeSingleRoute() and Optimize() methods, if both "cumul_values"</span></div>
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<div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">// and "cost" parameters are null, we don't optimize the cost and stop at the</span></div>
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<div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// first feasible solution in the LP (since in this case only feasibility is</span></div>
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<div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// of interest).</span></div>
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<div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#ad1ce954964d2775eb1adc6815d2e8928">OptimizeSingleRoute</a>(<span class="keywordtype">int</span> vehicle,</div>
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<div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  glop::LinearProgram* linear_program,</div>
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<div class="line"><a name="l00141"></a><span class="lineno"> 141</span>  glop::LPSolver* lp_solver,</div>
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<div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  std::vector<int64>* cumul_values, int64* cost,</div>
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<div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  int64* transit_cost, <span class="keywordtype">bool</span> clear_lp = <span class="keyword">true</span>);</div>
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<div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  </div>
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<div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#ad94c0819b78db0ff688cfb8c937c2a64">Optimize</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  glop::LinearProgram* linear_program, glop::LPSolver* lp_solver,</div>
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<div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  std::vector<int64>* cumul_values, int64* cost,</div>
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<div class="line"><a name="l00148"></a><span class="lineno"> 148</span>  int64* transit_cost, <span class="keywordtype">bool</span> clear_lp = <span class="keyword">true</span>);</div>
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<div class="line"><a name="l00149"></a><span class="lineno"> 149</span>  </div>
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<div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a201e3c7d1337f23f7825d9ec5bb6fe49">OptimizeAndPack</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  glop::LinearProgram* linear_program,</div>
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<div class="line"><a name="l00152"></a><span class="lineno"> 152</span>  glop::LPSolver* lp_solver,</div>
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<div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  std::vector<int64>* cumul_values);</div>
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<div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  </div>
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<div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0d9e33fb363c1a76d3510941f47bcbf5">OptimizeAndPackSingleRoute</a>(</div>
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<div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00157"></a><span class="lineno"> 157</span>  glop::LinearProgram* linear_program, glop::LPSolver* lp_solver,</div>
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<div class="line"><a name="l00158"></a><span class="lineno"> 158</span>  std::vector<int64>* cumul_values);</div>
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<div class="line"><a name="l00159"></a><span class="lineno"> 159</span>  </div>
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<div class="line"><a name="l00160"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70"> 160</a></span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> dimension_; }</div>
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<div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  </div>
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<div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="keyword">private</span>:</div>
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<div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// Initializes the containers and given linear program. Must be called prior</span></div>
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<div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// to setting any contraints and solving.</span></div>
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<div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="keywordtype">void</span> InitOptimizer(glop::LinearProgram* linear_program);</div>
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<div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  </div>
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<div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="comment">// Computes the minimum/maximum of cumuls for nodes on "route", and sets them</span></div>
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<div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="comment">// in current_route_[min|max]_cumuls_ respectively.</span></div>
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<div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  <span class="comment">// If the propagator_ is not null, uses the bounds tightened by the</span></div>
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<div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// propagator.</span></div>
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<div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// Otherwise, the bounds are computed by going over the nodes on the route</span></div>
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<div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// using the CP bounds, and the fixed transits are used to tighten them.</span></div>
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<div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="keywordtype">bool</span> ComputeRouteCumulBounds(<span class="keyword">const</span> std::vector<int64>& route,</div>
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<div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keyword">const</span> std::vector<int64>& fixed_transits,</div>
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<div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  int64 cumul_offset);</div>
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<div class="line"><a name="l00176"></a><span class="lineno"> 176</span>  </div>
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<div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Sets the constraints for all nodes on "vehicle"'s route according to</span></div>
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<div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// "next_accessor". If optimize_costs is true, also sets the objective</span></div>
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<div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="comment">// coefficients for the LP.</span></div>
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<div class="line"><a name="l00180"></a><span class="lineno"> 180</span>  <span class="comment">// Returns false if some infeasibility was detected, true otherwise.</span></div>
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<div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="keywordtype">bool</span> SetRouteCumulConstraints(</div>
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<div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  int64 cumul_offset, <span class="keywordtype">bool</span> optimize_costs,</div>
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<div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  glop::LinearProgram* linear_program, int64* route_transit_cost,</div>
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<div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  int64* route_cost_offset);</div>
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<div class="line"><a name="l00186"></a><span class="lineno"> 186</span>  </div>
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<div class="line"><a name="l00187"></a><span class="lineno"> 187</span>  <span class="comment">// Sets the global constraints on the dimension, and adds global objective</span></div>
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<div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="comment">// cost coefficients if optimize_costs is true.</span></div>
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<div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  <span class="comment">// NOTE: When called, the call to this function MUST come after</span></div>
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<div class="line"><a name="l00190"></a><span class="lineno"> 190</span>  <span class="comment">// SetRouteCumulConstraints() has been called on all routes, so that</span></div>
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<div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="comment">// index_to_cumul_variable_ and min_start/max_end_cumul_ are correctly</span></div>
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<div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  <span class="comment">// initialized.</span></div>
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<div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  <span class="keywordtype">void</span> SetGlobalConstraints(<span class="keywordtype">bool</span> optimize_costs,</div>
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<div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  glop::LinearProgram* linear_program);</div>
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<div class="line"><a name="l00195"></a><span class="lineno"> 195</span>  </div>
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<div class="line"><a name="l00196"></a><span class="lineno"> 196</span>  <span class="keywordtype">bool</span> FinalizeAndSolve(glop::LinearProgram* linear_program,</div>
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<div class="line"><a name="l00197"></a><span class="lineno"> 197</span>  glop::LPSolver* lp_solver);</div>
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<div class="line"><a name="l00198"></a><span class="lineno"> 198</span>  </div>
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<div class="line"><a name="l00199"></a><span class="lineno"> 199</span>  <span class="keywordtype">void</span> SetCumulValuesFromLP(<span class="keyword">const</span> std::vector<glop::ColIndex>& cumul_variables,</div>
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<div class="line"><a name="l00200"></a><span class="lineno"> 200</span>  int64 offset, <span class="keyword">const</span> glop::LPSolver& lp_solver,</div>
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<div class="line"><a name="l00201"></a><span class="lineno"> 201</span>  std::vector<int64>* cumul_values);</div>
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<div class="line"><a name="l00202"></a><span class="lineno"> 202</span>  </div>
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<div class="line"><a name="l00203"></a><span class="lineno"> 203</span>  <span class="comment">// This function packs the routes of the given vehicles while keeping the cost</span></div>
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<div class="line"><a name="l00204"></a><span class="lineno"> 204</span>  <span class="comment">// of the LP lower than its current (supposed optimal) objective value.</span></div>
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<div class="line"><a name="l00205"></a><span class="lineno"> 205</span>  <span class="comment">// It does so by setting the current objective variables' coefficient to 0 and</span></div>
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<div class="line"><a name="l00206"></a><span class="lineno"> 206</span>  <span class="comment">// setting the coefficient of the route ends to 1, to first minimize the route</span></div>
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<div class="line"><a name="l00207"></a><span class="lineno"> 207</span>  <span class="comment">// ends' cumuls, and then maximizes the starts' cumuls without increasing the</span></div>
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<div class="line"><a name="l00208"></a><span class="lineno"> 208</span>  <span class="comment">// ends.</span></div>
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<div class="line"><a name="l00209"></a><span class="lineno"> 209</span>  <span class="keywordtype">bool</span> PackRoutes(std::vector<int> vehicles,</div>
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<div class="line"><a name="l00210"></a><span class="lineno"> 210</span>  glop::LinearProgram* linear_program,</div>
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<div class="line"><a name="l00211"></a><span class="lineno"> 211</span>  glop::LPSolver* lp_solver);</div>
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<div class="line"><a name="l00212"></a><span class="lineno"> 212</span>  </div>
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<div class="line"><a name="l00213"></a><span class="lineno"> 213</span>  std::unique_ptr<CumulBoundsPropagator> propagator_;</div>
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<div class="line"><a name="l00214"></a><span class="lineno"> 214</span>  std::vector<int64> current_route_min_cumuls_;</div>
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<div class="line"><a name="l00215"></a><span class="lineno"> 215</span>  std::vector<int64> current_route_max_cumuls_;</div>
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<div class="line"><a name="l00216"></a><span class="lineno"> 216</span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <span class="keyword">const</span> dimension_;</div>
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<div class="line"><a name="l00217"></a><span class="lineno"> 217</span>  std::vector<glop::ColIndex> current_route_cumul_variables_;</div>
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<div class="line"><a name="l00218"></a><span class="lineno"> 218</span>  std::vector<glop::ColIndex> index_to_cumul_variable_;</div>
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<div class="line"><a name="l00219"></a><span class="lineno"> 219</span>  glop::ColIndex max_end_cumul_;</div>
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<div class="line"><a name="l00220"></a><span class="lineno"> 220</span>  glop::ColIndex min_start_cumul_;</div>
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<div class="line"><a name="l00221"></a><span class="lineno"> 221</span>  std::vector<std::pair<int64, int64>></div>
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<div class="line"><a name="l00222"></a><span class="lineno"> 222</span>  visited_pickup_delivery_indices_for_pair_;</div>
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<div class="line"><a name="l00223"></a><span class="lineno"> 223</span> };</div>
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<div class="line"><a name="l00224"></a><span class="lineno"> 224</span>  </div>
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<div class="line"><a name="l00225"></a><span class="lineno"> 225</span> <span class="comment">// Class used to compute optimal values for dimension cumuls of routes,</span></div>
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<div class="line"><a name="l00226"></a><span class="lineno"> 226</span> <span class="comment">// minimizing cumul soft lower and upper bound costs, and vehicle span costs of</span></div>
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<div class="line"><a name="l00227"></a><span class="lineno"> 227</span> <span class="comment">// a route.</span></div>
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<div class="line"><a name="l00228"></a><span class="lineno"> 228</span> <span class="comment">// In its methods, next_accessor is a callback returning the next node of a</span></div>
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<div class="line"><a name="l00229"></a><span class="lineno"> 229</span> <span class="comment">// given node on a route.</span></div>
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<div class="line"><a name="l00230"></a><span class="lineno"><a class="line" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html"> 230</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html">LocalDimensionCumulOptimizer</a> {</div>
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<div class="line"><a name="l00231"></a><span class="lineno"> 231</span>  <span class="keyword">public</span>:</div>
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<div class="line"><a name="l00232"></a><span class="lineno"> 232</span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a4e96037ef118cff2ca7a7207528bc551">LocalDimensionCumulOptimizer</a>(<span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>);</div>
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<div class="line"><a name="l00233"></a><span class="lineno"> 233</span>  </div>
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<div class="line"><a name="l00234"></a><span class="lineno"> 234</span>  <span class="comment">// If feasible, computes the optimal cost of the route performed by a vehicle,</span></div>
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<div class="line"><a name="l00235"></a><span class="lineno"> 235</span>  <span class="comment">// minimizing cumul soft lower and upper bound costs and vehicle span costs,</span></div>
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<div class="line"><a name="l00236"></a><span class="lineno"> 236</span>  <span class="comment">// and stores it in "optimal_cost" (if not null).</span></div>
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<div class="line"><a name="l00237"></a><span class="lineno"> 237</span>  <span class="comment">// Returns true iff the route respects all constraints.</span></div>
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<div class="line"><a name="l00238"></a><span class="lineno"> 238</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#aa6ddd99225cb62373aacf1057730fcff">ComputeRouteCumulCost</a>(<span class="keywordtype">int</span> vehicle,</div>
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<div class="line"><a name="l00239"></a><span class="lineno"> 239</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00240"></a><span class="lineno"> 240</span>  int64* optimal_cost);</div>
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<div class="line"><a name="l00241"></a><span class="lineno"> 241</span>  </div>
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<div class="line"><a name="l00242"></a><span class="lineno"> 242</span>  <span class="comment">// Same as ComputeRouteCumulCost, but the cost computed does not contain</span></div>
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<div class="line"><a name="l00243"></a><span class="lineno"> 243</span>  <span class="comment">// the part of the vehicle span cost due to fixed transits.</span></div>
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<div class="line"><a name="l00244"></a><span class="lineno"> 244</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a9f90927d3bc9aba01b6a5c54c72628fe">ComputeRouteCumulCostWithoutFixedTransits</a>(</div>
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<div class="line"><a name="l00245"></a><span class="lineno"> 245</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00246"></a><span class="lineno"> 246</span>  int64* optimal_cost_without_transits);</div>
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<div class="line"><a name="l00247"></a><span class="lineno"> 247</span>  </div>
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<div class="line"><a name="l00248"></a><span class="lineno"> 248</span>  <span class="comment">// If feasible, computes the optimal cumul values of the route performed by a</span></div>
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<div class="line"><a name="l00249"></a><span class="lineno"> 249</span>  <span class="comment">// vehicle, minimizing cumul soft lower and upper bound costs and vehicle span</span></div>
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<div class="line"><a name="l00250"></a><span class="lineno"> 250</span>  <span class="comment">// costs, stores them in "optimal_cumuls" (if not null), and returns true.</span></div>
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<div class="line"><a name="l00251"></a><span class="lineno"> 251</span>  <span class="comment">// Returns false if the route is not feasible.</span></div>
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<div class="line"><a name="l00252"></a><span class="lineno"> 252</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a499705f86a4018e42145f40b0c9124bf">ComputeRouteCumuls</a>(<span class="keywordtype">int</span> vehicle,</div>
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<div class="line"><a name="l00253"></a><span class="lineno"> 253</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00254"></a><span class="lineno"> 254</span>  std::vector<int64>* optimal_cumuls);</div>
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<div class="line"><a name="l00255"></a><span class="lineno"> 255</span>  </div>
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<div class="line"><a name="l00256"></a><span class="lineno"> 256</span>  <span class="comment">// Similar to ComputeRouteCumuls, but also tries to pack the cumul values on</span></div>
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<div class="line"><a name="l00257"></a><span class="lineno"> 257</span>  <span class="comment">// the route, such that the cost remains the same, the cumul of route end is</span></div>
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<div class="line"><a name="l00258"></a><span class="lineno"> 258</span>  <span class="comment">// minimized, and then the cumul of the start of the route is maximized.</span></div>
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<div class="line"><a name="l00259"></a><span class="lineno"> 259</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#af81c2a90a2e67c0b2ffcbfc15310912d">ComputePackedRouteCumuls</a>(</div>
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<div class="line"><a name="l00260"></a><span class="lineno"> 260</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00261"></a><span class="lineno"> 261</span>  std::vector<int64>* packed_cumuls);</div>
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<div class="line"><a name="l00262"></a><span class="lineno"> 262</span>  </div>
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<div class="line"><a name="l00263"></a><span class="lineno"><a class="line" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70"> 263</a></span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00264"></a><span class="lineno"> 264</span>  <span class="keywordflow">return</span> optimizer_core_.<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>();</div>
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<div class="line"><a name="l00265"></a><span class="lineno"> 265</span>  }</div>
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<div class="line"><a name="l00266"></a><span class="lineno"> 266</span>  </div>
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<div class="line"><a name="l00267"></a><span class="lineno"> 267</span>  <span class="keyword">private</span>:</div>
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<div class="line"><a name="l00268"></a><span class="lineno"> 268</span>  std::vector<std::unique_ptr<glop::LPSolver>> lp_solver_;</div>
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<div class="line"><a name="l00269"></a><span class="lineno"> 269</span>  std::vector<std::unique_ptr<glop::LinearProgram>> linear_program_;</div>
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<div class="line"><a name="l00270"></a><span class="lineno"> 270</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> optimizer_core_;</div>
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<div class="line"><a name="l00271"></a><span class="lineno"> 271</span> };</div>
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<div class="line"><a name="l00272"></a><span class="lineno"> 272</span>  </div>
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<div class="line"><a name="l00273"></a><span class="lineno"><a class="line" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html"> 273</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html">GlobalDimensionCumulOptimizer</a> {</div>
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<div class="line"><a name="l00274"></a><span class="lineno"> 274</span>  <span class="keyword">public</span>:</div>
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<div class="line"><a name="l00275"></a><span class="lineno"> 275</span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a69c482660dedcd9e709e459c979790a2">GlobalDimensionCumulOptimizer</a>(<span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>);</div>
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<div class="line"><a name="l00276"></a><span class="lineno"> 276</span>  <span class="comment">// If feasible, computes the optimal cost of the entire model with regards to</span></div>
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<div class="line"><a name="l00277"></a><span class="lineno"> 277</span>  <span class="comment">// the optimizer_core_'s dimension costs, minimizing cumul soft lower/upper</span></div>
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<div class="line"><a name="l00278"></a><span class="lineno"> 278</span>  <span class="comment">// bound costs and vehicle/global span costs, and stores it in "optimal_cost"</span></div>
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<div class="line"><a name="l00279"></a><span class="lineno"> 279</span>  <span class="comment">// (if not null).</span></div>
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<div class="line"><a name="l00280"></a><span class="lineno"> 280</span>  <span class="comment">// Returns true iff all the constraints can be respected.</span></div>
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<div class="line"><a name="l00281"></a><span class="lineno"> 281</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a90da6b10d66522633bfeefc1289cf05c">ComputeCumulCostWithoutFixedTransits</a>(</div>
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<div class="line"><a name="l00282"></a><span class="lineno"> 282</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00283"></a><span class="lineno"> 283</span>  int64* optimal_cost_without_transits);</div>
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<div class="line"><a name="l00284"></a><span class="lineno"> 284</span>  <span class="comment">// If feasible, computes the optimal cumul values, minimizing cumul soft</span></div>
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<div class="line"><a name="l00285"></a><span class="lineno"> 285</span>  <span class="comment">// lower/upper bound costs and vehicle/global span costs, stores them in</span></div>
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<div class="line"><a name="l00286"></a><span class="lineno"> 286</span>  <span class="comment">// "optimal_cumuls" (if not null), and returns true.</span></div>
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<div class="line"><a name="l00287"></a><span class="lineno"> 287</span>  <span class="comment">// Returns false if the routes are not feasible.</span></div>
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<div class="line"><a name="l00288"></a><span class="lineno"> 288</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ad38d4817624f537041e9bb1d306768e2">ComputeCumuls</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00289"></a><span class="lineno"> 289</span>  std::vector<int64>* optimal_cumuls);</div>
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<div class="line"><a name="l00290"></a><span class="lineno"> 290</span>  </div>
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<div class="line"><a name="l00291"></a><span class="lineno"> 291</span>  <span class="comment">// Returns true iff the routes resulting from the next_accessor are feasible</span></div>
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<div class="line"><a name="l00292"></a><span class="lineno"> 292</span>  <span class="comment">// wrt the constraints on the optimizer_core_.dimension()'s cumuls.</span></div>
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<div class="line"><a name="l00293"></a><span class="lineno"> 293</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a501d5aa6957f63484392048ba566b0a3">IsFeasible</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor);</div>
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<div class="line"><a name="l00294"></a><span class="lineno"> 294</span>  </div>
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<div class="line"><a name="l00295"></a><span class="lineno"> 295</span>  <span class="comment">// Similar to ComputeCumuls, but also tries to pack the cumul values on all</span></div>
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<div class="line"><a name="l00296"></a><span class="lineno"> 296</span>  <span class="comment">// routes, such that the cost remains the same, the cumuls of route ends are</span></div>
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<div class="line"><a name="l00297"></a><span class="lineno"> 297</span>  <span class="comment">// minimized, and then the cumuls of the starts of the routes are maximized.</span></div>
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<div class="line"><a name="l00298"></a><span class="lineno"> 298</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ad61ae74484849042d200dbb1dc0d0116">ComputePackedCumuls</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div>
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<div class="line"><a name="l00299"></a><span class="lineno"> 299</span>  std::vector<int64>* packed_cumuls);</div>
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<div class="line"><a name="l00300"></a><span class="lineno"> 300</span>  </div>
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<div class="line"><a name="l00301"></a><span class="lineno"><a class="line" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70"> 301</a></span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>()<span class="keyword"> const </span>{</div>
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<div class="line"><a name="l00302"></a><span class="lineno"> 302</span>  <span class="keywordflow">return</span> optimizer_core_.<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">dimension</a>();</div>
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<div class="line"><a name="l00303"></a><span class="lineno"> 303</span>  }</div>
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<div class="line"><a name="l00304"></a><span class="lineno"> 304</span>  </div>
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<div class="line"><a name="l00305"></a><span class="lineno"> 305</span>  <span class="keyword">private</span>:</div>
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<div class="line"><a name="l00306"></a><span class="lineno"> 306</span>  glop::LPSolver lp_solver_;</div>
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<div class="line"><a name="l00307"></a><span class="lineno"> 307</span>  glop::LinearProgram linear_program_;</div>
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<div class="line"><a name="l00308"></a><span class="lineno"> 308</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> optimizer_core_;</div>
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<div class="line"><a name="l00309"></a><span class="lineno"> 309</span> };</div>
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<div class="line"><a name="l00310"></a><span class="lineno"> 310</span>  </div>
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<div class="line"><a name="l00311"></a><span class="lineno"> 311</span> } <span class="comment">// namespace operations_research</span></div>
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<div class="line"><a name="l00312"></a><span class="lineno"> 312</span>  </div>
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<div class="line"><a name="l00313"></a><span class="lineno"> 313</span> <span class="preprocessor">#endif // OR_TOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_</span></div>
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</div><!-- fragment --></div><!-- contents -->
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_a6769f38bbc3271d45b0512c12ef31f70"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">operations_research::LocalDimensionCumulOptimizer::dimension</a></div><div class="ttdeci">const RoutingDimension * dimension() const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00263">routing_lp_scheduling.h:263</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html">operations_research::GlobalDimensionCumulOptimizer</a></div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00273">routing_lp_scheduling.h:273</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html_a72be8a813404822957fbd808d0405226"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html#a72be8a813404822957fbd808d0405226">operations_research::CumulBoundsPropagator::dimension</a></div><div class="ttdeci">const RoutingDimension & dimension() const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00050">routing_lp_scheduling.h:50</a></div></div>
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<div class="ttc" id="arouting_8h_html"><div class="ttname"><a href="routing_8h.html">routing.h</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_a5967f72ac911a2ed971a1ce103d1c47b"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a5967f72ac911a2ed971a1ce103d1c47b">operations_research::DimensionCumulOptimizerCore::DimensionCumulOptimizerCore</a></div><div class="ttdeci">DimensionCumulOptimizerCore(const RoutingDimension *dimension, bool use_precedence_propagator)</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00124">routing_lp_scheduling.h:124</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_a4e96037ef118cff2ca7a7207528bc551"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a4e96037ef118cff2ca7a7207528bc551">operations_research::LocalDimensionCumulOptimizer::LocalDimensionCumulOptimizer</a></div><div class="ttdeci">LocalDimensionCumulOptimizer(const RoutingDimension *dimension)</div></div>
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<div class="ttc" id="anamespaceoperations__research_html"><div class="ttname"><a href="namespaceoperations__research.html">operations_research</a></div><div class="ttdoc">The vehicle routing library lets one model and solve generic vehicle routing problems ranging from th...</div><div class="ttdef"><b>Definition:</b> <a href="constraint__solver_8h_source.html#l00092">constraint_solver.h:92</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_a6769f38bbc3271d45b0512c12ef31f70"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a6769f38bbc3271d45b0512c12ef31f70">operations_research::GlobalDimensionCumulOptimizer::dimension</a></div><div class="ttdeci">const RoutingDimension * dimension() const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00301">routing_lp_scheduling.h:301</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_a90da6b10d66522633bfeefc1289cf05c"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a90da6b10d66522633bfeefc1289cf05c">operations_research::GlobalDimensionCumulOptimizer::ComputeCumulCostWithoutFixedTransits</a></div><div class="ttdeci">bool ComputeCumulCostWithoutFixedTransits(const std::function< int64(int64)> &next_accessor, int64 *optimal_cost_without_transits)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_a499705f86a4018e42145f40b0c9124bf"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a499705f86a4018e42145f40b0c9124bf">operations_research::LocalDimensionCumulOptimizer::ComputeRouteCumuls</a></div><div class="ttdeci">bool ComputeRouteCumuls(int vehicle, const std::function< int64(int64)> &next_accessor, std::vector< int64 > *optimal_cumuls)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_a501d5aa6957f63484392048ba566b0a3"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a501d5aa6957f63484392048ba566b0a3">operations_research::GlobalDimensionCumulOptimizer::IsFeasible</a></div><div class="ttdeci">bool IsFeasible(const std::function< int64(int64)> &next_accessor)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html_a921806c74f3dd622afc937929b69d627"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html#a921806c74f3dd622afc937929b69d627">operations_research::CumulBoundsPropagator::CumulMin</a></div><div class="ttdeci">int64 CumulMin(int index) const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00041">routing_lp_scheduling.h:41</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_ad1ce954964d2775eb1adc6815d2e8928"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#ad1ce954964d2775eb1adc6815d2e8928">operations_research::DimensionCumulOptimizerCore::OptimizeSingleRoute</a></div><div class="ttdeci">bool OptimizeSingleRoute(int vehicle, const std::function< int64(int64)> &next_accessor, glop::LinearProgram *linear_program, glop::LPSolver *lp_solver, std::vector< int64 > *cumul_values, int64 *cost, int64 *transit_cost, bool clear_lp=true)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html">operations_research::LocalDimensionCumulOptimizer</a></div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00230">routing_lp_scheduling.h:230</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_aa6ddd99225cb62373aacf1057730fcff"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#aa6ddd99225cb62373aacf1057730fcff">operations_research::LocalDimensionCumulOptimizer::ComputeRouteCumulCost</a></div><div class="ttdeci">bool ComputeRouteCumulCost(int vehicle, const std::function< int64(int64)> &next_accessor, int64 *optimal_cost)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html">operations_research::DimensionCumulOptimizerCore</a></div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00122">routing_lp_scheduling.h:122</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1RoutingDimension_html"><div class="ttname"><a href="classoperations__research_1_1RoutingDimension.html">operations_research::RoutingDimension</a></div><div class="ttdoc">Dimensions represent quantities accumulated at nodes along the routes.</div><div class="ttdef"><b>Definition:</b> <a href="routing_8h_source.html#l02058">routing.h:2058</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html_a54688e4af975568b0bce87cdf9f26781"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html#a54688e4af975568b0bce87cdf9f26781">operations_research::CumulBoundsPropagator::PropagateCumulBounds</a></div><div class="ttdeci">bool PropagateCumulBounds(const std::function< int64(int64)> &next_accessor, int64 cumul_offset)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_ad38d4817624f537041e9bb1d306768e2"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ad38d4817624f537041e9bb1d306768e2">operations_research::GlobalDimensionCumulOptimizer::ComputeCumuls</a></div><div class="ttdeci">bool ComputeCumuls(const std::function< int64(int64)> &next_accessor, std::vector< int64 > *optimal_cumuls)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html_aff8f29b2fce9f6447474ee6077af1b72"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html#aff8f29b2fce9f6447474ee6077af1b72">operations_research::CumulBoundsPropagator::CumulBoundsPropagator</a></div><div class="ttdeci">CumulBoundsPropagator(const RoutingDimension *dimension)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_a201e3c7d1337f23f7825d9ec5bb6fe49"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a201e3c7d1337f23f7825d9ec5bb6fe49">operations_research::DimensionCumulOptimizerCore::OptimizeAndPack</a></div><div class="ttdeci">bool OptimizeAndPack(const std::function< int64(int64)> &next_accessor, glop::LinearProgram *linear_program, glop::LPSolver *lp_solver, std::vector< int64 > *cumul_values)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_ad94c0819b78db0ff688cfb8c937c2a64"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#ad94c0819b78db0ff688cfb8c937c2a64">operations_research::DimensionCumulOptimizerCore::Optimize</a></div><div class="ttdeci">bool Optimize(const std::function< int64(int64)> &next_accessor, glop::LinearProgram *linear_program, glop::LPSolver *lp_solver, std::vector< int64 > *cumul_values, int64 *cost, int64 *transit_cost, bool clear_lp=true)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_a9f90927d3bc9aba01b6a5c54c72628fe"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a9f90927d3bc9aba01b6a5c54c72628fe">operations_research::LocalDimensionCumulOptimizer::ComputeRouteCumulCostWithoutFixedTransits</a></div><div class="ttdeci">bool ComputeRouteCumulCostWithoutFixedTransits(int vehicle, const std::function< int64(int64)> &next_accessor, int64 *optimal_cost_without_transits)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html">operations_research::CumulBoundsPropagator</a></div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00028">routing_lp_scheduling.h:28</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1LocalDimensionCumulOptimizer_html_af81c2a90a2e67c0b2ffcbfc15310912d"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#af81c2a90a2e67c0b2ffcbfc15310912d">operations_research::LocalDimensionCumulOptimizer::ComputePackedRouteCumuls</a></div><div class="ttdeci">bool ComputePackedRouteCumuls(int vehicle, const std::function< int64(int64)> &next_accessor, std::vector< int64 > *packed_cumuls)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1CumulBoundsPropagator_html_a3b5250c6480d24af0c95d1f68a7556b7"><div class="ttname"><a href="classoperations__research_1_1CumulBoundsPropagator.html#a3b5250c6480d24af0c95d1f68a7556b7">operations_research::CumulBoundsPropagator::CumulMax</a></div><div class="ttdeci">int64 CumulMax(int index) const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00045">routing_lp_scheduling.h:45</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_a0d9e33fb363c1a76d3510941f47bcbf5"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0d9e33fb363c1a76d3510941f47bcbf5">operations_research::DimensionCumulOptimizerCore::OptimizeAndPackSingleRoute</a></div><div class="ttdeci">bool OptimizeAndPackSingleRoute(int vehicle, const std::function< int64(int64)> &next_accessor, glop::LinearProgram *linear_program, glop::LPSolver *lp_solver, std::vector< int64 > *cumul_values)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1DimensionCumulOptimizerCore_html_a6769f38bbc3271d45b0512c12ef31f70"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a6769f38bbc3271d45b0512c12ef31f70">operations_research::DimensionCumulOptimizerCore::dimension</a></div><div class="ttdeci">const RoutingDimension * dimension() const</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00160">routing_lp_scheduling.h:160</a></div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_ad61ae74484849042d200dbb1dc0d0116"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ad61ae74484849042d200dbb1dc0d0116">operations_research::GlobalDimensionCumulOptimizer::ComputePackedCumuls</a></div><div class="ttdeci">bool ComputePackedCumuls(const std::function< int64(int64)> &next_accessor, std::vector< int64 > *packed_cumuls)</div></div>
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<div class="ttc" id="aclassoperations__research_1_1GlobalDimensionCumulOptimizer_html_a69c482660dedcd9e709e459c979790a2"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a69c482660dedcd9e709e459c979790a2">operations_research::GlobalDimensionCumulOptimizer::GlobalDimensionCumulOptimizer</a></div><div class="ttdeci">GlobalDimensionCumulOptimizer(const RoutingDimension *dimension)</div></div>
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