1. Vehicle breakdown Change in transportation requests Arrival of new transportation requests Disruption on infrastructure impact of incident duration of incident Set of autonomous vehicles Transportation infrastructure of resources Set of transportation requests: for each request, find a conflict-free shortest path head-on conflicts catching-up conflicts Taking into account malfunctioning vehicles and resources Problem description Incidents before / afterCPU SpeedPerformanceRobustness TR+-- FPS+0- TWGR- / 0+ Traffic rules and zone control (TR) Only allow actions that can never lead to a conflict Fixed path scheduling (FPS) Insert waiting times for best schedule along pre-determined path Job Shop Scheduling with blocking Time window graph routing (TWGR) Search through graph of free time windows Avoiding planned movements of others Introduce slack into routing schedules Multi-objective routing: efficiency reliability Robustness of planning and execution method combined Impact of bi-directional resources Dest. Factory shop floor Transportation order from one machine to another bi-directional lanes dynamic environment AGV container terminal Highly structured infrastructure Transportation orders in large batches Airport taxiing Congestion in peak hours and exceptional weather conditions Multi-stage routing problem Mostly uni-directional taxiways Smart conflict prevention: from explicit constraint checking to off-line encoding of constraints into free time windows Complexity: from O(n 2 v 4 ) to O(nv · log(nv) + n 2 v) Better spread of agents over space and time results in better performance A*-search through graph of free time windows Fixed Path Scheduling is very fast, but Time Window Graph Routing also finds a solution within 0.5s. Repeated use of Fixed Path algorithms leads to overuse of key resources. Time Window Graph routing provides spread of agents over space and time. TWGR is an optimal planning algorithm for a single-agent. Order in which agents plan is important for individual plan quality. For the Schiphol airport experiments, the order in which agents plan is not of great importance to system performance. The red truck has reserved its plan. Now, the blue truck wants to plan a route to its destination e4. Context-Aware Logistic Routing and Scheduling Adriaan ter Mors, Almende Jonne Zutt, Cees Witteveen, Algorithmics, Faculty of EEMCS, TU Delft Adriaan ter Mors Jonne Zutt Cees Witteveen adriaan@almende.org j.zutt@tudelft.nl c.witteveen@tudelft.nl Gridlock From a2 two free time windows in b2 can be reached. Only the later window can reach a free time window in c2. abcde 1 2 3 4 5 The shortest-time path is to go via a5. TWGR with heuristic TWGR no heuristic TWGR no cycles FPS TWGR with heuristic TWGR no heuristic TWGR no cycles FPS TWGR with heuristic TWGR no heuristic TWGR no cycles FPS TWGR with heuristic TWGR no heuristic TWGR no cycles FPS Example time window graph TWGR versus FPS Total vs. single plan quality Example time window graph Coping with incidents Time window graph routing Methods