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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><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><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><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><div class="line"><a name="l00005"></a><span class="lineno"> 5</span> <span class="comment">//</span></div><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><div class="line"><a name="l00007"></a><span class="lineno"> 7</span> <span class="comment">//</span></div><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><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><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><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><div class="line"><a name="l00012"></a><span class="lineno"> 12</span> <span class="comment">// limitations under the License.</span></div><div class="line"><a name="l00013"></a><span class="lineno"> 13</span> </div><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><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><div class="line"><a name="l00016"></a><span class="lineno"> 16</span> </div><div class="line"><a name="l00017"></a><span class="lineno"> 17</span> <span class="preprocessor">#include "<a class="code" href="routing_8h.html">ortools/constraint_solver/routing.h</a>"</span></div><div class="line"><a name="l00018"></a><span class="lineno"> 18</span> <span class="preprocessor">#include "ortools/glop/lp_solver.h"</span></div><div class="line"><a name="l00019"></a><span class="lineno"> 19</span> </div><div class="line"><a name="l00020"></a><span class="lineno"> 20</span> <span class="keyword">namespace </span><a class="code" href="namespaceoperations__research.html">operations_research</a> {</div><div class="line"><a name="l00021"></a><span class="lineno"> 21</span> </div><div class="line"><a name="l00022"></a><span class="lineno"> 22</span> <span class="comment">// Classes to solve dimension cumul placement (aka scheduling) problems using</span></div><div class="line"><a name="l00023"></a><span class="lineno"> 23</span> <span class="comment">// linear programming.</span></div><div class="line"><a name="l00024"></a><span class="lineno"> 24</span> </div><div class="line"><a name="l00025"></a><span class="lineno"> 25</span> <span class="comment">// Utility class used in Local/GlobalDimensionCumulOptimizer to set the LP</span></div><div class="line"><a name="l00026"></a><span class="lineno"> 26</span> <span class="comment">// constraints and solve the problem.</span></div><div class="line"><a name="l00027"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html"> 27</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> {</div><div class="line"><a name="l00028"></a><span class="lineno"> 28</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00029"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a36e89016a79a172ccf35fd2cd91496df"> 29</a></span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a36e89016a79a172ccf35fd2cd91496df">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#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>)</div><div class="line"><a name="l00030"></a><span class="lineno"> 30</span>  : dimension_(<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>),</div><div class="line"><a name="l00031"></a><span class="lineno"> 31</span>  visited_pickup_index_for_pair_(</div><div class="line"><a name="l00032"></a><span class="lineno"> 32</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>->model()->GetPickupAndDeliveryPairs().size(), -1) {}</div><div class="line"><a name="l00033"></a><span class="lineno"> 33</span> </div><div class="line"><a name="l00034"></a><span class="lineno"> 34</span>  <span class="comment">// In the OptimizeSingleRoute() and Optimize() methods, if both "cumul_values"</span></div><div class="line"><a name="l00035"></a><span class="lineno"> 35</span>  <span class="comment">// and "cost" parameters are null, we don't optimize the cost and stop at the</span></div><div class="line"><a name="l00036"></a><span class="lineno"> 36</span>  <span class="comment">// first feasible solution in the LP (since in this case only feasibility is</span></div><div class="line"><a name="l00037"></a><span class="lineno"> 37</span>  <span class="comment">// of interest).</span></div><div class="line"><a name="l00038"></a><span class="lineno"> 38</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a4a7c9b3881b45490cb56078fa661f4a9">OptimizeSingleRoute</a>(<span class="keywordtype">int</span> vehicle,</div><div class="line"><a name="l00039"></a><span class="lineno"> 39</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00040"></a><span class="lineno"> 40</span>  glop::LinearProgram* linear_program,</div><div class="line"><a name="l00041"></a><span class="lineno"> 41</span>  glop::LPSolver* lp_solver,</div><div class="line"><a name="l00042"></a><span class="lineno"> 42</span>  std::vector<int64>* cumul_values, int64* cost,</div><div class="line"><a name="l00043"></a><span class="lineno"> 43</span>  int64* transit_cost, <span class="keywordtype">bool</span> clear_lp = <span class="keyword">true</span>);</div><div class="line"><a name="l00044"></a><span class="lineno"> 44</span> </div><div class="line"><a name="l00045"></a><span class="lineno"> 45</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e128419cfa4b416e1b13f780f2e2477">Optimize</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00046"></a><span class="lineno"> 46</span>  glop::LinearProgram* linear_program, glop::LPSolver* lp_solver,</div><div class="line"><a name="l00047"></a><span class="lineno"> 47</span>  std::vector<int64>* cumul_values, int64* cost,</div><div class="line"><a name="l00048"></a><span class="lineno"> 48</span>  int64* transit_cost, <span class="keywordtype">bool</span> clear_lp = <span class="keyword">true</span>);</div><div class="line"><a name="l00049"></a><span class="lineno"> 49</span> </div><div class="line"><a name="l00050"></a><span class="lineno"> 50</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0817496a096c399614e3e95780d82087">OptimizeAndPack</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00051"></a><span class="lineno"> 51</span>  glop::LinearProgram* linear_program,</div><div class="line"><a name="l00052"></a><span class="lineno"> 52</span>  glop::LPSolver* lp_solver,</div><div class="line"><a name="l00053"></a><span class="lineno"> 53</span>  std::vector<int64>* cumul_values);</div><div class="line"><a name="l00054"></a><span class="lineno"> 54</span> </div><div class="line"><a name="l00055"></a><span class="lineno"> 55</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a83da37cfbbf38554e2e59089df384e7c">OptimizeAndPackSingleRoute</a>(</div><div class="line"><a name="l00056"></a><span class="lineno"> 56</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00057"></a><span class="lineno"> 57</span>  glop::LinearProgram* linear_program, glop::LPSolver* lp_solver,</div><div class="line"><a name="l00058"></a><span class="lineno"> 58</span>  std::vector<int64>* cumul_values);</div><div class="line"><a name="l00059"></a><span class="lineno"> 59</span> </div><div class="line"><a name="l00060"></a><span class="lineno"><a class="line" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea"> 60</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#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> dimension_; }</div><div class="line"><a name="l00061"></a><span class="lineno"> 61</span> </div><div class="line"><a name="l00062"></a><span class="lineno"> 62</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00063"></a><span class="lineno"> 63</span>  <span class="comment">// Initializes the containers and given linear program. Must be called prior</span></div><div class="line"><a name="l00064"></a><span class="lineno"> 64</span>  <span class="comment">// to setting any contraints and solving.</span></div><div class="line"><a name="l00065"></a><span class="lineno"> 65</span>  <span class="keywordtype">void</span> InitOptimizer(glop::LinearProgram* linear_program);</div><div class="line"><a name="l00066"></a><span class="lineno"> 66</span> </div><div class="line"><a name="l00067"></a><span class="lineno"> 67</span>  <span class="comment">// Sets the constraints for all nodes on "vehicle"'s route according to</span></div><div class="line"><a name="l00068"></a><span class="lineno"> 68</span>  <span class="comment">// "next_accessor". If optimize_costs is true, also sets the objective</span></div><div class="line"><a name="l00069"></a><span class="lineno"> 69</span>  <span class="comment">// coefficients for the LP.</span></div><div class="line"><a name="l00070"></a><span class="lineno"> 70</span>  <span class="comment">// Returns false if some infeasibility was detected, true otherwise.</span></div><div class="line"><a name="l00071"></a><span class="lineno"> 71</span>  <span class="keywordtype">bool</span> SetRouteCumulConstraints(</div><div class="line"><a name="l00072"></a><span class="lineno"> 72</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00073"></a><span class="lineno"> 73</span>  int64 cumul_offset, <span class="keywordtype">bool</span> optimize_costs,</div><div class="line"><a name="l00074"></a><span class="lineno"> 74</span>  glop::LinearProgram* linear_program, int64* route_transit_cost,</div><div class="line"><a name="l00075"></a><span class="lineno"> 75</span>  int64* route_cost_offset);</div><div class="line"><a name="l00076"></a><span class="lineno"> 76</span> </div><div class="line"><a name="l00077"></a><span class="lineno"> 77</span>  <span class="comment">// Sets the global constraints on the dimension, and adds global objective</span></div><div class="line"><a name="l00078"></a><span class="lineno"> 78</span>  <span class="comment">// cost coefficients if optimize_costs is true.</span></div><div class="line"><a name="l00079"></a><span class="lineno"> 79</span>  <span class="comment">// NOTE: When called, the call to this function MUST come after</span></div><div class="line"><a name="l00080"></a><span class="lineno"> 80</span>  <span class="comment">// SetRouteCumulConstraints() has been called on all routes, so that</span></div><div class="line"><a name="l00081"></a><span class="lineno"> 81</span>  <span class="comment">// index_to_cumul_variable_ and min_start/max_end_cumul_ are correctly</span></div><div class="line"><a name="l00082"></a><span class="lineno"> 82</span>  <span class="comment">// initialized.</span></div><div class="line"><a name="l00083"></a><span class="lineno"> 83</span>  <span class="keywordtype">void</span> SetGlobalConstraints(<span class="keywordtype">bool</span> optimize_costs,</div><div class="line"><a name="l00084"></a><span class="lineno"> 84</span>  glop::LinearProgram* linear_program);</div><div class="line"><a name="l00085"></a><span class="lineno"> 85</span> </div><div class="line"><a name="l00086"></a><span class="lineno"> 86</span>  <span class="keywordtype">bool</span> FinalizeAndSolve(glop::LinearProgram* linear_program,</div><div class="line"><a name="l00087"></a><span class="lineno"> 87</span>  glop::LPSolver* lp_solver);</div><div class="line"><a name="l00088"></a><span class="lineno"> 88</span> </div><div class="line"><a name="l00089"></a><span class="lineno"> 89</span>  <span class="keywordtype">void</span> SetCumulValuesFromLP(<span class="keyword">const</span> std::vector<glop::ColIndex>& cumul_variables,</div><div class="line"><a name="l00090"></a><span class="lineno"> 90</span>  int64 offset, <span class="keyword">const</span> glop::LPSolver& lp_solver,</div><div class="line"><a name="l00091"></a><span class="lineno"> 91</span>  std::vector<int64>* cumul_values);</div><div class="line"><a name="l00092"></a><span class="lineno"> 92</span> </div><div class="line"><a name="l00093"></a><span class="lineno"> 93</span>  <span class="comment">// This function packs the routes of the given vehicles while keeping the cost</span></div><div class="line"><a name="l00094"></a><span class="lineno"> 94</span>  <span class="comment">// of the LP lower than its current (supposed optimal) objective value.</span></div><div class="line"><a name="l00095"></a><span class="lineno"> 95</span>  <span class="comment">// It does so by setting the current objective variables' coefficient to 0 and</span></div><div class="line"><a name="l00096"></a><span class="lineno"> 96</span>  <span class="comment">// setting the coefficient of the route ends to 1, to first minimize the route</span></div><div class="line"><a name="l00097"></a><span class="lineno"> 97</span>  <span class="comment">// ends' cumuls, and then maximizes the starts' cumuls without increasing the</span></div><div class="line"><a name="l00098"></a><span class="lineno"> 98</span>  <span class="comment">// ends.</span></div><div class="line"><a name="l00099"></a><span class="lineno"> 99</span>  <span class="keywordtype">bool</span> PackRoutes(std::vector<int> vehicles,</div><div class="line"><a name="l00100"></a><span class="lineno"> 100</span>  glop::LinearProgram* linear_program,</div><div class="line"><a name="l00101"></a><span class="lineno"> 101</span>  glop::LPSolver* lp_solver);</div><div class="line"><a name="l00102"></a><span class="lineno"> 102</span> </div><div class="line"><a name="l00103"></a><span class="lineno"> 103</span>  <span class="keyword">const</span> <a class="code" href="classoperations__research_1_1RoutingDimension.html">RoutingDimension</a>* <span class="keyword">const</span> dimension_;</div><div class="line"><a name="l00104"></a><span class="lineno"> 104</span>  std::vector<glop::ColIndex> current_route_cumul_variables_;</div><div class="line"><a name="l00105"></a><span class="lineno"> 105</span>  std::vector<glop::ColIndex> index_to_cumul_variable_;</div><div class="line"><a name="l00106"></a><span class="lineno"> 106</span>  glop::ColIndex max_end_cumul_;</div><div class="line"><a name="l00107"></a><span class="lineno"> 107</span>  glop::ColIndex min_start_cumul_;</div><div class="line"><a name="l00108"></a><span class="lineno"> 108</span>  std::vector<int64> visited_pickup_index_for_pair_;</div><div class="line"><a name="l00109"></a><span class="lineno"> 109</span> };</div><div class="line"><a name="l00110"></a><span class="lineno"> 110</span> </div><div class="line"><a name="l00111"></a><span class="lineno"> 111</span> <span class="comment">// Class used to compute optimal values for dimension cumuls of routes,</span></div><div class="line"><a name="l00112"></a><span class="lineno"> 112</span> <span class="comment">// minimizing cumul soft lower and upper bound costs, and vehicle span costs of</span></div><div class="line"><a name="l00113"></a><span class="lineno"> 113</span> <span class="comment">// a route.</span></div><div class="line"><a name="l00114"></a><span class="lineno"> 114</span> <span class="comment">// In its methods, next_accessor is a callback returning the next node of a</span></div><div class="line"><a name="l00115"></a><span class="lineno"> 115</span> <span class="comment">// given node on a route.</span></div><div class="line"><a name="l00116"></a><span class="lineno"><a class="line" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html"> 116</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html">LocalDimensionCumulOptimizer</a> {</div><div class="line"><a name="l00117"></a><span class="lineno"> 117</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00118"></a><span class="lineno"> 118</span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#ac4fce7a68e479fab75cf1d5e161d75df">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#a9771b7daec71c086f027561e905a447e">dimension</a>);</div><div class="line"><a name="l00119"></a><span class="lineno"> 119</span> </div><div class="line"><a name="l00120"></a><span class="lineno"> 120</span>  <span class="comment">// If feasible, computes the optimal cost of the route performed by a vehicle,</span></div><div class="line"><a name="l00121"></a><span class="lineno"> 121</span>  <span class="comment">// minimizing cumul soft lower and upper bound costs and vehicle span costs,</span></div><div class="line"><a name="l00122"></a><span class="lineno"> 122</span>  <span class="comment">// and stores it in "optimal_cost" (if not null).</span></div><div class="line"><a name="l00123"></a><span class="lineno"> 123</span>  <span class="comment">// Returns true iff the route respects all constraints.</span></div><div class="line"><a name="l00124"></a><span class="lineno"> 124</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a61efb881f6d454c92969b636860c3ec0">ComputeRouteCumulCost</a>(<span class="keywordtype">int</span> vehicle,</div><div class="line"><a name="l00125"></a><span class="lineno"> 125</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00126"></a><span class="lineno"> 126</span>  int64* optimal_cost);</div><div class="line"><a name="l00127"></a><span class="lineno"> 127</span> </div><div class="line"><a name="l00128"></a><span class="lineno"> 128</span>  <span class="comment">// Same as ComputeRouteCumulCost, but the cost computed does not contain</span></div><div class="line"><a name="l00129"></a><span class="lineno"> 129</span>  <span class="comment">// the part of the vehicle span cost due to fixed transits.</span></div><div class="line"><a name="l00130"></a><span class="lineno"> 130</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a17d5586b9cbc444067c85f3a664edc94">ComputeRouteCumulCostWithoutFixedTransits</a>(</div><div class="line"><a name="l00131"></a><span class="lineno"> 131</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00132"></a><span class="lineno"> 132</span>  int64* optimal_cost_without_transits);</div><div class="line"><a name="l00133"></a><span class="lineno"> 133</span> </div><div class="line"><a name="l00134"></a><span class="lineno"> 134</span>  <span class="comment">// If feasible, computes the optimal cumul values of the route performed by a</span></div><div class="line"><a name="l00135"></a><span class="lineno"> 135</span>  <span class="comment">// vehicle, minimizing cumul soft lower and upper bound costs and vehicle span</span></div><div class="line"><a name="l00136"></a><span class="lineno"> 136</span>  <span class="comment">// costs, stores them in "optimal_cumuls" (if not null), and returns true.</span></div><div class="line"><a name="l00137"></a><span class="lineno"> 137</span>  <span class="comment">// Returns false if the route is not feasible.</span></div><div class="line"><a name="l00138"></a><span class="lineno"> 138</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#aac604b6944a3226c09b8227cfcd95e41">ComputeRouteCumuls</a>(<span class="keywordtype">int</span> vehicle,</div><div class="line"><a name="l00139"></a><span class="lineno"> 139</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00140"></a><span class="lineno"> 140</span>  std::vector<int64>* optimal_cumuls);</div><div class="line"><a name="l00141"></a><span class="lineno"> 141</span> </div><div class="line"><a name="l00142"></a><span class="lineno"> 142</span>  <span class="comment">// Similar to ComputeRouteCumuls, but also tries to pack the cumul values on</span></div><div class="line"><a name="l00143"></a><span class="lineno"> 143</span>  <span class="comment">// the route, such that the cost remains the same, the cumul of route end is</span></div><div class="line"><a name="l00144"></a><span class="lineno"> 144</span>  <span class="comment">// minimized, and then the cumul of the start of the route is maximized.</span></div><div class="line"><a name="l00145"></a><span class="lineno"> 145</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a80bde55511799f3e2b2e3d82c92b1514">ComputePackedRouteCumuls</a>(</div><div class="line"><a name="l00146"></a><span class="lineno"> 146</span>  <span class="keywordtype">int</span> vehicle, <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00147"></a><span class="lineno"> 147</span>  std::vector<int64>* packed_cumuls);</div><div class="line"><a name="l00148"></a><span class="lineno"> 148</span> </div><div class="line"><a name="l00149"></a><span class="lineno"><a class="line" href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a9771b7daec71c086f027561e905a447e"> 149</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#a9771b7daec71c086f027561e905a447e">dimension</a>()<span class="keyword"> const </span>{</div><div class="line"><a name="l00150"></a><span class="lineno"> 150</span>  <span class="keywordflow">return</span> optimizer_core_.<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>();</div><div class="line"><a name="l00151"></a><span class="lineno"> 151</span>  }</div><div class="line"><a name="l00152"></a><span class="lineno"> 152</span> </div><div class="line"><a name="l00153"></a><span class="lineno"> 153</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00154"></a><span class="lineno"> 154</span>  std::vector<std::unique_ptr<glop::LPSolver>> lp_solver_;</div><div class="line"><a name="l00155"></a><span class="lineno"> 155</span>  std::vector<std::unique_ptr<glop::LinearProgram>> linear_program_;</div><div class="line"><a name="l00156"></a><span class="lineno"> 156</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> optimizer_core_;</div><div class="line"><a name="l00157"></a><span class="lineno"> 157</span> };</div><div class="line"><a name="l00158"></a><span class="lineno"> 158</span> </div><div class="line"><a name="l00159"></a><span class="lineno"><a class="line" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html"> 159</a></span> <span class="keyword">class </span><a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html">GlobalDimensionCumulOptimizer</a> {</div><div class="line"><a name="l00160"></a><span class="lineno"> 160</span>  <span class="keyword">public</span>:</div><div class="line"><a name="l00161"></a><span class="lineno"> 161</span>  <span class="keyword">explicit</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a7aef4229a23f8e4527a1abdd40792a60">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#a64246ca0403f93006288ead58dfb0d36">dimension</a>);</div><div class="line"><a name="l00162"></a><span class="lineno"> 162</span>  <span class="comment">// If feasible, computes the optimal cost of the entire model with regards to</span></div><div class="line"><a name="l00163"></a><span class="lineno"> 163</span>  <span class="comment">// the optimizer_core_'s dimension costs, minimizing cumul soft lower/upper</span></div><div class="line"><a name="l00164"></a><span class="lineno"> 164</span>  <span class="comment">// bound costs and vehicle/global span costs, and stores it in "optimal_cost"</span></div><div class="line"><a name="l00165"></a><span class="lineno"> 165</span>  <span class="comment">// (if not null).</span></div><div class="line"><a name="l00166"></a><span class="lineno"> 166</span>  <span class="comment">// Returns true iff all the constraints can be respected.</span></div><div class="line"><a name="l00167"></a><span class="lineno"> 167</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a58e57b4acd1b8657912b8cb08a52a6ba">ComputeCumulCostWithoutFixedTransits</a>(</div><div class="line"><a name="l00168"></a><span class="lineno"> 168</span>  <span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00169"></a><span class="lineno"> 169</span>  int64* optimal_cost_without_transits);</div><div class="line"><a name="l00170"></a><span class="lineno"> 170</span>  <span class="comment">// If feasible, computes the optimal cumul values, minimizing cumul soft</span></div><div class="line"><a name="l00171"></a><span class="lineno"> 171</span>  <span class="comment">// lower/upper bound costs and vehicle/global span costs, stores them in</span></div><div class="line"><a name="l00172"></a><span class="lineno"> 172</span>  <span class="comment">// "optimal_cumuls" (if not null), and returns true.</span></div><div class="line"><a name="l00173"></a><span class="lineno"> 173</span>  <span class="comment">// Returns false if the routes are not feasible.</span></div><div class="line"><a name="l00174"></a><span class="lineno"> 174</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ae661c23f8b98bdcf659aba760638a965">ComputeCumuls</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00175"></a><span class="lineno"> 175</span>  std::vector<int64>* optimal_cumuls);</div><div class="line"><a name="l00176"></a><span class="lineno"> 176</span> </div><div class="line"><a name="l00177"></a><span class="lineno"> 177</span>  <span class="comment">// Returns true iff the routes resulting from the next_accessor are feasible</span></div><div class="line"><a name="l00178"></a><span class="lineno"> 178</span>  <span class="comment">// wrt the constraints on the optimizer_core_.dimension()'s cumuls.</span></div><div class="line"><a name="l00179"></a><span class="lineno"> 179</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ac584fa126a3b1b582bd918a516b489f9">IsFeasible</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor);</div><div class="line"><a name="l00180"></a><span class="lineno"> 180</span> </div><div class="line"><a name="l00181"></a><span class="lineno"> 181</span>  <span class="comment">// Similar to ComputeCumuls, but also tries to pack the cumul values on all</span></div><div class="line"><a name="l00182"></a><span class="lineno"> 182</span>  <span class="comment">// routes, such that the cost remains the same, the cumuls of route ends are</span></div><div class="line"><a name="l00183"></a><span class="lineno"> 183</span>  <span class="comment">// minimized, and then the cumuls of the starts of the routes are maximized.</span></div><div class="line"><a name="l00184"></a><span class="lineno"> 184</span>  <span class="keywordtype">bool</span> <a class="code" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a4609e630ca9d2d7340a338a957d44e61">ComputePackedCumuls</a>(<span class="keyword">const</span> std::function<int64(int64)>& next_accessor,</div><div class="line"><a name="l00185"></a><span class="lineno"> 185</span>  std::vector<int64>* packed_cumuls);</div><div class="line"><a name="l00186"></a><span class="lineno"> 186</span> </div><div class="line"><a name="l00187"></a><span class="lineno"><a class="line" href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a64246ca0403f93006288ead58dfb0d36"> 187</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#a64246ca0403f93006288ead58dfb0d36">dimension</a>()<span class="keyword"> const </span>{</div><div class="line"><a name="l00188"></a><span class="lineno"> 188</span>  <span class="keywordflow">return</span> optimizer_core_.<a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea">dimension</a>();</div><div class="line"><a name="l00189"></a><span class="lineno"> 189</span>  }</div><div class="line"><a name="l00190"></a><span class="lineno"> 190</span> </div><div class="line"><a name="l00191"></a><span class="lineno"> 191</span>  <span class="keyword">private</span>:</div><div class="line"><a name="l00192"></a><span class="lineno"> 192</span>  glop::LPSolver lp_solver_;</div><div class="line"><a name="l00193"></a><span class="lineno"> 193</span>  glop::LinearProgram linear_program_;</div><div class="line"><a name="l00194"></a><span class="lineno"> 194</span>  <a class="code" href="classoperations__research_1_1DimensionCumulOptimizerCore.html">DimensionCumulOptimizerCore</a> optimizer_core_;</div><div class="line"><a name="l00195"></a><span class="lineno"> 195</span> };</div><div class="line"><a name="l00196"></a><span class="lineno"> 196</span> </div><div class="line"><a name="l00197"></a><span class="lineno"> 197</span> } <span class="comment">// namespace operations_research</span></div><div class="line"><a name="l00198"></a><span class="lineno"> 198</span> </div><div class="line"><a name="l00199"></a><span class="lineno"> 199</span> <span class="preprocessor">#endif // OR_TOOLS_CONSTRAINT_SOLVER_ROUTING_LP_SCHEDULING_H_</span></div><div class="ttc" id="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_a4609e630ca9d2d7340a338a957d44e61"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a4609e630ca9d2d7340a338a957d44e61">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="classoperations__research_1_1LocalDimensionCumulOptimizer_html_ac4fce7a68e479fab75cf1d5e161d75df"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#ac4fce7a68e479fab75cf1d5e161d75df">operations_research::LocalDimensionCumulOptimizer::LocalDimensionCumulOptimizer</a></div><div class="ttdeci">LocalDimensionCumulOptimizer(const RoutingDimension *dimension)</div></div>
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<div class="ttc" id="classoperations__research_1_1DimensionCumulOptimizerCore_html_a0e80b6dfc017f25d413603e7aeda52ea"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e80b6dfc017f25d413603e7aeda52ea">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#l00060">routing_lp_scheduling.h:60</a></div></div>
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<div class="ttc" id="classoperations__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#l02023">routing.h:2023</a></div></div>
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<div class="ttc" id="classoperations__research_1_1LocalDimensionCumulOptimizer_html_aac604b6944a3226c09b8227cfcd95e41"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#aac604b6944a3226c09b8227cfcd95e41">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="classoperations__research_1_1LocalDimensionCumulOptimizer_html_a80bde55511799f3e2b2e3d82c92b1514"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a80bde55511799f3e2b2e3d82c92b1514">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="classoperations__research_1_1DimensionCumulOptimizerCore_html_a0e128419cfa4b416e1b13f780f2e2477"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0e128419cfa4b416e1b13f780f2e2477">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="classoperations__research_1_1DimensionCumulOptimizerCore_html_a0817496a096c399614e3e95780d82087"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a0817496a096c399614e3e95780d82087">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="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_a64246ca0403f93006288ead58dfb0d36"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a64246ca0403f93006288ead58dfb0d36">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#l00187">routing_lp_scheduling.h:187</a></div></div>
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<div class="ttc" id="classoperations__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#l00159">routing_lp_scheduling.h:159</a></div></div>
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<div class="ttc" id="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_ac584fa126a3b1b582bd918a516b489f9"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ac584fa126a3b1b582bd918a516b489f9">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="routing_8h_html"><div class="ttname"><a href="routing_8h.html">routing.h</a></div></div>
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<div class="ttc" id="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_a7aef4229a23f8e4527a1abdd40792a60"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a7aef4229a23f8e4527a1abdd40792a60">operations_research::GlobalDimensionCumulOptimizer::GlobalDimensionCumulOptimizer</a></div><div class="ttdeci">GlobalDimensionCumulOptimizer(const RoutingDimension *dimension)</div></div>
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<div class="ttc" id="classoperations__research_1_1DimensionCumulOptimizerCore_html_a83da37cfbbf38554e2e59089df384e7c"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a83da37cfbbf38554e2e59089df384e7c">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="classoperations__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#l00116">routing_lp_scheduling.h:116</a></div></div>
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<div class="ttc" id="classoperations__research_1_1LocalDimensionCumulOptimizer_html_a17d5586b9cbc444067c85f3a664edc94"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a17d5586b9cbc444067c85f3a664edc94">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="classoperations__research_1_1LocalDimensionCumulOptimizer_html_a61efb881f6d454c92969b636860c3ec0"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a61efb881f6d454c92969b636860c3ec0">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="classoperations__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#l00027">routing_lp_scheduling.h:27</a></div></div>
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<div class="ttc" id="namespaceoperations__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="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_a58e57b4acd1b8657912b8cb08a52a6ba"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#a58e57b4acd1b8657912b8cb08a52a6ba">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="classoperations__research_1_1LocalDimensionCumulOptimizer_html_a9771b7daec71c086f027561e905a447e"><div class="ttname"><a href="classoperations__research_1_1LocalDimensionCumulOptimizer.html#a9771b7daec71c086f027561e905a447e">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#l00149">routing_lp_scheduling.h:149</a></div></div>
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<div class="ttc" id="classoperations__research_1_1DimensionCumulOptimizerCore_html_a36e89016a79a172ccf35fd2cd91496df"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a36e89016a79a172ccf35fd2cd91496df">operations_research::DimensionCumulOptimizerCore::DimensionCumulOptimizerCore</a></div><div class="ttdeci">DimensionCumulOptimizerCore(const RoutingDimension *dimension)</div><div class="ttdef"><b>Definition:</b> <a href="routing__lp__scheduling_8h_source.html#l00029">routing_lp_scheduling.h:29</a></div></div>
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<div class="ttc" id="classoperations__research_1_1DimensionCumulOptimizerCore_html_a4a7c9b3881b45490cb56078fa661f4a9"><div class="ttname"><a href="classoperations__research_1_1DimensionCumulOptimizerCore.html#a4a7c9b3881b45490cb56078fa661f4a9">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="classoperations__research_1_1GlobalDimensionCumulOptimizer_html_ae661c23f8b98bdcf659aba760638a965"><div class="ttname"><a href="classoperations__research_1_1GlobalDimensionCumulOptimizer.html#ae661c23f8b98bdcf659aba760638a965">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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