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Ice Road Routing: A Complex Problem

North Slope Decision Support System: Ice Road Planning Algorithms 3 rd Stakeholder Workshop April 27-28, 2011. Ice Road Routing: A Complex Problem. Often multiple exploration sites Sometimes multiple possible starting points Avoidance of undesirable/difficult features Water availability

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Ice Road Routing: A Complex Problem

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  1. North Slope Decision Support System: Ice Road Planning Algorithms3rd Stakeholder WorkshopApril 27-28, 2011

  2. Ice Road Routing: A Complex Problem • Often multiple exploration sites • Sometimes multiple possible starting points • Avoidance of undesirable/difficult features • Water availability • Regulatory issues • Path re-use in future seasons • Multiple objectives • Cost • Time • Risk/Reliability

  3. END START

  4. Algorithm Development • Ant Colony Systems • Based on natural behavior of ants in route finding between nest and food… Pheromones!

  5. Algorithm Development • Ant Colony Systems • Based on natural behavior of ants in route finding between nest and food… Pheromones!

  6. Algorithm Development • Ant Colony Systems • Based on natural behavior of ants in route finding between nest and food… Pheromones!

  7. Algorithm Development • Ant Colony Systems • Based on natural behavior of ants in route finding between nest and food… Pheromones!

  8. Issues to Solve • Ants get lost in complex and open topologies • Most traditional ACS applications are single objective • Our real-world ants have to build their own roads and need water

  9. Graph Pruning • Keep ants from getting lost by reducing topology to non-redundant paths

  10. Graph Pruning • Keep ants from getting lost by reducing topology to non-redundant paths

  11. Multi-Objective Path-Finding • Traditional ACS includes link choice heurisitics • Exploration versus Exploitation • Probabilistic Choice weighted by pheromones and a priori suitability scores Pher2 Suit2 Pher1 Suit1 Pher3 Suit3

  12. Multi-Objective Path-Finding Multi-objective ACS includes multiple suitability scores, preference functions, and non-dominated sorting to find pareto-fronts Pher2 SuitA2 SuitB2 SuitC2 Obj B Pher3 SuitA3 SuitB3 SuitC3 Pher1 SuitA1 SuitB1 SuitC1 Obj A

  13. Water Accounting As the ants move along the path, they search for the nearest available permitted lake to withdraw water from. 2 2 4 1 1 3 3

  14. Path Straightening On large open grids, sometimes the paths will have unnecessary bends or curves.

  15. Multiple Start Points The ants can determine the best starting point given a stretch of road.

  16. Multiple End Points Steiner Problem – Minimum Spanning Tree Waypoint

  17. Multiple End Points Steiner Problem – Minimum Spanning Tree

  18. Multiple End Points When there are more than 3 endpoints then pheromone sharing is used.

  19. END START

  20. END START

  21. END START

  22. Limitations The resolution of the roads are limited by the 100m x 100m DEM data used. Water accounting for road construction is dependent upon available lake data. Some data may not be available.

  23. Demonstration

  24. How does NSDSS fit into the planning process? 1. Review “Permitting Matrix” 2. Let’s sketch out planning process and identify tasks that NSDSS can help improve

  25. “Permitting Matrix”

  26. Planning Process

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