STP: An Aerial Spray Treatment Planning System W.D. Potter, Ramyaa, J. Li Artificial Intelligence Center, GSRC 111 University of Georgia, Athens, GA (Contact: or And J. Ghent, D. Twardus, H. Thistle USDA Forest Service

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

Abstract The Spray Treatment Planner – an intelligent decision support system for aerial spray treatment. A tool to schedule spraying pesticides aerially - a capacitated vehicle router STP schedules the spraying operation of selected blocks from selected airports using single or multiple aircraft. The scheduling is done to maximize the spray efficiency and spray productivity by minimizing the total time and distance flown. It uses heuristics to obtain a near optimal solution.

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

The gypsy moth (Lymantria dispar L.) has been one of north American’s most devastating forest pests. Application of pesticides by aircraft. Determining the production needs - guess work or heuristics from other projects. Over-estimating contract needs - larger than needed aircraft or more aircraft than needed. Under-estimating contract needs - treatment at less than optimal timing. Needs careful preparation and planning, as well as comparing different spray application strategies. A classic problem to be solved by AI techniques. How STP came about

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

STP – a capacitated vehicle router Attempts to give an optimal schedule for sprayingoptimal Schedule is restricted by fuel and pesticide tank capacity Comparison of different schedules Gives a realistic estimate of productivity and needs Comparison of productivity of various aircraft explain Goals of STP

airport blocks

airport blocks

STP – a capacitated vehicle router Gives an optimal schedule for sprayingoptimal Schedule is restricted by fuel and pesticide tank capacity Comparison of different schedules Gives a realistic estimate of productivity and needs Comparison of productivity of various aircraft explain Goals of STP

Optimal schedule : Quantitative measures of effectiveness Spray productivity = area sprayed total aerial spray operation time Spray efficiency = time spent spraying total aerial spray operation time Total aerial = time spent + ferry spray time spraying time Optimize : ferry time ; time spent spraying

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

The evaluation model flowchart

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

Heuristics Minimize total flying distance Minimize time spent in a block : “Flight Advisor” for a single block“Flight Advisor” Minimize ferry time representation core of the heuristics justification implementation

Flight Advisor If the block = polygon of rectangles then spray along the longest side of rectangles else spray along the longest side of the polygon endif

Heuristics Minimize total flying distance Minimize time spent in a block : “Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

Representation G = {V,E} – a connected graph V = {V1-Vn} a block set E = {(Vi,Vi)} a set of flight lines Lij – length of flight line Vi-Vj Qi – load associated with Vi Minimize a linear combination of total distance traveled by different aircraft Restricted by pesticide and fuel capacity

Heuristics Minimize total flying distance Minimize time spent in a block : “Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

Case 1 (typical case): The blocks are on the same side of the airport but the airport and the blocks are not in the same line. The total saved flight distance is: D1+D2-D3. Distance saved =D1+D2-D3

Case 2 (worst case): The blocks are on different sides of the airport, and the airport and blocks are in one line so that the total reduced distance is 0. Nothing saved

Case 3 (best case): The blocks are on the same side and in the same line with respect to the airport. The total saved distance in this case is D1+D2+D3.

Heuristics Minimize total flying distance Minimize time spent in a block : “Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

The Capacitated Vehicle Routing Problem is the Traveling Salesperson Problem with additional constraints of capacity Exact calculation is not possible for large inputs Basnet (1997) gives 2 heuristics and shows that heuristics give reasonably close answers to the exact ones Justification

Heuristics Minimize total flying distance Minimize time spent in a block : Flight Advisor” for a single block Minimize ferry time representation core of the heuristics justification implementation

Implementation CVRPS CVRPS -- for multiple blocks serviced by a single airport CVRPM -- multiple airports

Capacitated Vehicle Routing Problem for Multiple Blocks Serviced by Single Airport Forms initial runs such that each run services a single block and associates runs to blocks For each run 1) try combining the closest block 2) combination successful if the capacity constraints are met for each run (new combined run) calculate the full schedule as a collection of runs and choose the best

Implementation CVRPS -- for multiple blocks serviced by a single airport CVRPMCVRPM -- for multiple airports

An extension of CVRPS using one airport as base or home airport and the rest for fueling or restocking pesticides. Works by relaxing the constraints in capacity Capacitated Vehicle Routing Problem for Multiple Blocks Serviced by Single Airport

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

Overview of Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments

Conclusion & future work The spray advisor uses heuristic methods to find near optimal schedules for spraying selected blocks This project is in progress Some of the future areas of development involve 1)Considering the terrain to be sprayed 2)Considering mixed aircraft 3)Considering preferred direction of flight

Thanks

Overview of the Presentation Abstract How did STP come about Goals of STP Basic architecture Heuristics Overview of STP Conclusion and future developments