Project Presentation by Eddie Smolyansky & Shilo Abramovitch Supervisor: David Erdos.

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Presentation transcript:

Project Presentation by Eddie Smolyansky & Shilo Abramovitch Supervisor: David Erdos

 Project definition  Previous solutions  Work environment & interface  Our solution  Finding shortest paths  Building initial solution  Moving in solution space  Taboo search  Results & Discussion  Summary  Improvements & Future work

 Vehicle Routing Problem  With Time Windows  Assumptions  Complication: No Fly Zones  Complex combinatorial optimization problem.

 Background  Greedy  Genetic Algorithms  Simulated Annealing  Taboo Search  combinations

 Main code written in C++  Graphical output using MATLAB  Input, output and interface between programs in form of text files

 It has been proven reliable  Simple and understandable concept  Easy to modify and improve

 Finding the shortest paths between points and their “costs”  Finding an initial solution to the problem  Trying to improve that solution

 Finding the cost of going directly between all two points (including NFZ polygon points)  Allowing to pass through one more NFZ polygon points in each iteration  Along the way saving all the minimum costs (time/distance) and the shortest paths in a matrix

 Start with an empty route and add waypoints as long as possible  The waypoints we chose are those that maximize the time difference  Then we start with a fresh route until we finish with all the way points

 Discarding all empty routes  Trying to insert all the way-points of a route to the others  Upon success in discarding a route we start from the beginning of the stage

 Taboo search  A greedy search  Stop upon reaching local minima

 Reversing the optimizing direction  Restarting the search upon reaching a local maximum  A fast break out but does not guarantee finding a new local minimum

 As many points as needed in each polygon  Any kind of polygons, convex or not  Any kind of combination of polygons, overlapping or not

 Difficulties with assessing results Instance# WaypointsCapacityRun- Time# UAVsBenchmark r sec19 c sec10 rc sec1514 r sec33 c sec33 c sec10

 Versatile algorithm  Very fast  Quality results  Surpassed expectations

 The problem  Finding shortest paths  Building initial solution  Moving in solution space  Taboo search  Results & capabilities

 Graphical User Interface  Soft time windows  Improved coding (object oriented)

 Questions?