| 288 | = Set the new Evaluator in HeuristicLab = |

| 289 | |

| 290 | After we finished implementing the insertion infos and the evaluator, compile the plugin and start HeuristicLab. |

| 291 | |

| 292 | 1. Open a VRP sample (e.g. Tabu Search – VRP) |

| 293 | a. Choose a CVPR instance (e.g. Augerat A-n36-k5) |

| 294 | 2. Show hidden parameters in problem |

| 295 | a. Select the Evaluator parameter and set the value to the new PRPEvaluator |

| 296 | 3. In the ProblemInstance set following parameter |

| 297 | a. Limit the number of Vehicles (e.g. 5) (depends on problem; compare the number of vehicles for best known quality) |

| 298 | b. Set DistanceFactor to 0 (distance is already considered in energy consumption) |

| 299 | 4. Set parameter of the algorithm |

| 300 | a. Increase number of Iterations (e.g. 20000) |

| 301 | 5. Start the algorithm |

| 302 | |

| 303 | Now the algorithm tries to find routs where the stops with high demands are delivered first to avoid carrying around heavy loads. (Do demonstrate the effect cities with greater demands are displayed bigger.) |

| 304 | |

| 305 | ||= with PRP =|| |

| 306 | || [[Image(6-PRP_result.png, 600px)]] || |

| 307 | |

| 308 | |

| 309 | Compared to the standard CVRP evaluator, the solution for PRP can differentiate significantly. |

| 310 | |

| 311 | ||= with CVRP =|| |

| 312 | || [[Image(7-CVRP_result.png, 600px)]] || |