Large-Scale 3D En-Route Conflict Resolution

Transcription

1 Large-Scale 3D En-Route Conflict Resolution Cyril Allignol, Nicolas Barnier, Nicolas Durand, Alexandre Gondran and Ruixin Wang ATM 2017 Seattle June 28 th, 2017

2 Background Introduction The Conflict Resolution Problem Research on automatic conflict resolution started in the 1980s Many different models comply with existing resolution techniques Research from ANSPs focused on realistic models, but not on resolution methods Other approaches focused both on model (e.g. using uncertainty models and BADA) and resolution algorithm, but completely tailored to a given traffic simulator (e.g. CATS) prevents scientific community from comparing different methods Many generic resolution algorithms able to deal with complex problems (e.g. simplex, Branch & Bound, metaheuristics...): should be tested and compared scientifically on the same instances Conflict resolution: large-scale combinatorial problem a common model needed to validate the comparison Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

3 Introduction The Conflict Resolution Problem A New Framework for Solving En-Route Conflicts [ATM 2013] Trajectory maneuver options + prediction with uncertainties Conflicts discrete detection combinatorial optimization problem Resolution solvers independent from models Benchmark enables scientific comparison of algorithms (e.g. CP vs GA) Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

4 Introduction The Conflict Resolution Problem A New Framework for Solving En-Route Conflicts [ATM 2013] Trajectory maneuver options + prediction with uncertainties Conflicts discrete detection combinatorial optimization problem Resolution solvers independent from models Benchmark enables scientific comparison of algorithms (e.g. CP vs GA) What s New... Scenarios 3D over several FLs with possible in-between waypoints Maneuvers more versatile, including FL change Uncertainties more taken into account Instances larger (up to 100 aircraft) Solver Memetic Algorithm: harder, better, faster, stronger! Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

5 Contents Introduction 1 Benchmark Trajectory Prediction Conflict Detection Conflict Resolution Problem 2 Resolution Data Algorithms Results 3 Conclusion Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

6 Traffic Benchmark Trajectory Prediction Initial Trajectories Levelled (in our scenarios, but could be climbing or descending) Following a sequence of waypoints Associated to a nominal aircraft speed Sampled into time steps of duration τ (small enough, e.g. 3 s) ready to be embedded in a traffic simulator FL 310 FL 300 FL 290 Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

7 Benchmark Trajectory Prediction Maneuver Model Maneuvers Starts at t 0 and returns on initial trajectory after t 1 Either change heading by α or change FL by δ FL For simplicity, heading and FL changes cannot be combined llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

8 Benchmark Trajectory Prediction Maneuver Model Maneuvers Starts at t 0 and returns on initial trajectory after t 1 Either change heading by α or change FL by δ FL For simplicity, heading and FL changes cannot be combined parameter size typical values start t 0 n 0 (= 4) 0,1,2,3 (min) return t 1 n 1 (= 4) 5,6,7,8 (min) horizontal α n α (= 6) -30,-20,-10,+10,+20,+30 ( ) vertical δ FL n FL (= 4) -20,-10,+10,+20 (FL) Options per aircraft: n man = n 0 n 1 (n α + n FL ) + 1 (= 161) t 0 t 0 α δfl t 1 t 1 llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

9 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

10 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} Reaction Time Uncertainty E t0 : start error t 0 + E t0 E t1 : return error t 0 t 1 t 1 + E t1 llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

11 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} Heading Change Uncertainty E α : angle error t 0 α α E α α + E α t 1 Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

12 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} Speed Uncertainty E vh : speed error (1 + Evh t )vh (1 0 Evh )vh (1 Evh )vh t 1 (1 + Evh )vh llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

13 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} Climb and Descend Uncertainty E vv : vertical error t (1 + E vv )v v (1 E vv )v v 0 New FL (1 + E vv )v v (1 E vv )v v t 1 Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

14 Handling Uncertainties Benchmark Trajectory Prediction Uncertainties on maneuvers and speed parameter error typical values start t 0 ε t0 [0, E t0 ] s return t 1 ε t1 [0, E t1 ] s angle α ε α [ E α, E α ] 1-3 horizontal speed v h ε vh [ E vh, E vh ] 2-6 % vertical speed v v ε vv [ E vv, E vv ] 5-15 % beacon fly mode f m f m {F b, F o } {F b, F o} Beacon Fly Mode F b : fly by F o : fly over F o F b Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

15 Trajectory Hull Model Benchmark Trajectory Prediction Horizontal Plane At each time step, aircraft position modelled as the smallest convex hull containing all possible positions Red: not maneuvered yet (ε vh ) Green: being maneuvered (ε t0, ε α ) Blue: returning (ε t1 ) llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

16 Trajectory Hull Model Benchmark Trajectory Prediction Horizontal Plane At each time step, aircraft position modelled as the smallest convex hull containing all possible positions Red: not maneuvered yet (ε vh ) Green: being maneuvered (ε t0, ε α ) Blue: returning (ε t1 ) Gray: smallest convex hull Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

17 Trajectory Hull Model Benchmark Horizontal Plane At each time step, aircraft position modelled as the smallest convex hull containing all possible positions Red: not maneuvered yet (ε vh ) Green: being maneuvered (ε t0, ε α ) Blue: returning (ε t1 ) Gray: smallest convex hull Trajectory Prediction Vertical Plane For simplicity, the 3D convex hull is approximated by the smallest right cylinder (prism) containing all possible horizontal convex hulls according to ε vv alt. max alt. min llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

18 Conflict Matrix Benchmark Conflict Detection Detection Trajectories l of aircraft i and k of aircraft j are conflicting iff t = k τ: dist v (ch(l, t), ch(k, t)) < norm v dist h (ch(l, t), ch(k, t)) < norm h where ch(l,t) is the 3D convex hull (prism) of trajectory l at time t, and typically norm v = 1000 ft and norm h = 5 NM Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

19 Conflict Matrix Benchmark Conflict Detection Detection Trajectories l of aircraft i and k of aircraft j are conflicting iff t = k τ: dist v (ch(l, t), ch(k, t)) < norm v dist h (ch(l, t), ch(k, t)) < norm h where ch(l,t) is the 3D convex hull (prism) of trajectory l at time t, and typically norm v = 1000 ft and norm h = 5 NM For all ordered pairs of aircraft and pairs of trajectories (i, j) [1, n] 2, i < j, (k, l) [1, n man ] 2 C i,j,k,l = { true false if trajectories k and l conflicts otherwise Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

20 Benchmark Conflict Resolution Problem Combinatorial Optimization Decision variables M = {m i, i [1, n]} with m i [1, n man ] All maneuver options associated with allowed tuples t 0, t 1, α, δ FL are numbered from 1 to n man m i represents the maneuver of aircraft i Size of the search space: n n man Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

21 Benchmark Combinatorial Optimization Conflict Resolution Problem Decision variables M = {m i, i [1, n]} with m i [1, n man ] All maneuver options associated with allowed tuples t 0, t 1, α, δ FL are numbered from 1 to n man m i represents the maneuver of aircraft i Size of the search space: n n man Cost cost(m) = n i=1 c(m i) Increasing absolute values of parameter indexed by k [1, n ] { (n Individual: c(m i ) = 0 k 0 ) 2 + k 2 kα 2 if α (1 + k δ ) 2 if δ FL 0 0 otherwise where m i is described by the tuple k 0, k 1, k α, k δ Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

22 Benchmark Combinatorial Optimization Conflict Resolution Problem Decision variables M = {m i, i [1, n]} with m i [1, n man ] All maneuver options associated with allowed tuples t 0, t 1, α, δ FL are numbered from 1 to n man m i represents the maneuver of aircraft i Size of the search space: n n man Cost cost(m) = n i=1 c(m i) Increasing absolute values of parameter indexed by k [1, n ] { 2 ( 4 1 ) Individual: c(m i ) = = 22 if α 0 (1 + k δ ) 2 if δ FL 0 0 otherwise where m i is described by the tuple 0 min, 7 min, 20, F L0 Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

23 Benchmark Combinatorial Optimization Conflict Resolution Problem Decision variables M = {m i, i [1, n]} with m i [1, n man ] All maneuver options associated with allowed tuples t 0, t 1, α, δ FL are numbered from 1 to n man m i represents the maneuver of aircraft i Size of the search space: n n man Cost cost(m) = n i=1 c(m i) Increasing absolute values of parameter indexed by k [1, n ] { (n Individual: c(m i ) = 0 k 0 ) 2 + k 2 kα 2 if α (1 + k δ ) 2 if δ FL 0 0 otherwise where m i is described by the tuple k 0, k 1, k α, k δ Constraints (i, j) [1, n] 2 s.t. i j C i,j,mi,m j with C i,j,k,l = maneuvers k of aircraft i and l of aircraft j conflicts Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

24 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

25 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

26 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

27 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

28 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

29 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

30 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

31 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

32 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

33 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

34 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

35 Benchmark Benchmark Conflict Resolution Problem Available at clusters.recherche.enac.fr Instance files: specified by matrix C for a given set of parameters Current results: optimal solutions, lower and upper bounds Currently n {5,..., 100}, n man = 161, 3 levels of uncertainty, 10 instances A Small Sample From a Benchmark File d c c m m Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

36 Contents Resolution 1 Benchmark Trajectory Prediction Conflict Detection Conflict Resolution Problem 2 Resolution Data Algorithms Results 3 Conclusion Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

37 Data Resolution Data New benchmark 3D instances with aircraft evenly dispatched over 5 FLs airspace 100 NM radius altitude from FL280 to FL320 speed randomly chosen within 20 % of 480 kn climb rate 600 ft min 1 From 5 to 100 aircraft Vertical maneuver options: climb or descend 1000 ft or 2000 ft Aircraft interfere with each other across FLs Harder than independent layers: search space and forbidden maneuvers pairs exponentially increase with the number of layers But in-between waypoints and beacon fly mode not tested yet... Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

38 Data Resolution Data New benchmark 3D instances with aircraft evenly dispatched over 5 FLs airspace 100 NM radius altitude from FL280 to FL320 speed randomly chosen within 20 % of 480 kn climb rate 600 ft min 1 From 5 to 100 aircraft Vertical maneuver options: climb or descend 1000 ft or 2000 ft Aircraft interfere with each other across FLs Harder than independent layers: search space and forbidden maneuvers pairs exponentially increase with the number of layers But in-between waypoints and beacon fly mode not tested yet... DEMO Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

39 Resolution Algorithms Memetic Algorithm (MA) [J.-K. Hao, 2012] Hybridization of Evolutionary Algorithm (EA) and Local Search (LS) Overall evolutionary framework Recombination: classic crossover with two parents Local improvement of a candidate: Tabu Search Each element of the population is a local minimum Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

40 Resolution Algorithms Memetic Algorithm (MA) [J.-K. Hao, 2012] Hybridization of Evolutionary Algorithm (EA) and Local Search (LS) Overall evolutionary framework Recombination: classic crossover with two parents Local improvement of a candidate: Tabu Search Each element of the population is a local minimum Tabu Search [F. Glover, 1986] Local Search with best neighbour selection Limited memory of forbidden moves to avoid cycling Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

41 Resolution Algorithms Memetic Algorithm (MA) [J.-K. Hao, 2012] Hybridization of Evolutionary Algorithm (EA) and Local Search (LS) Overall evolutionary framework Recombination: classic crossover with two parents Local improvement of a candidate: Tabu Search Each element of the population is a local minimum Tabu Search [F. Glover, 1986] Local Search with best neighbour selection Limited memory of forbidden moves to avoid cycling Fitness F (M) = M i<j C i,j,mi,m j + cost(m) M: a big (enough) integer to ensure that F (M 1 ) < F (M 2 ) iff M 2 has more conflicts than M 1 Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

42 Resolution Constraint Programming (CP) Algorithms CP Paradigm Separation between model and search strategies fast incremental prototyping Focused on combinatorial constraint satisfaction Complete algorithm: optimality or infeasibility proof but exponential in the worst case... Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

43 Resolution Constraint Programming (CP) Algorithms CP Paradigm Separation between model and search strategies fast incremental prototyping Focused on combinatorial constraint satisfaction Complete algorithm: optimality or infeasibility proof but exponential in the worst case... Results Improved solver implementation over [ATM 2013]: optimality proof for all original instances and new 3D ones up to 30 aircraft With 300 s time limit: optimal solution obtained, most proofs too long For more than 30 aircraft: challenging or even out of reach to find a first solution (except for infeasible 100-aircraft instances) Validates metaheuristics results for reasonable instances Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

44 Resolution Results Memetic Algorithm vs Constraint Programming CP MA mean execution time (s) number of aircraft Mean execution time (in seconds) to find the optimal solution MA and CP both obtain the optimal solution MA scales better with larger and harder instances Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

45 Resolution Global Cost of Best Solutions Results mean cost and extreme values ε = 1 ε = 2 ε = number of aircraft Mean cost found by the MA with 300 s time limit MA always obtains conflict-free solutions within the allocated time Optimal solution consistently reached whenever provable by CP Cost increases w.r.t. number of aircraft and uncertainty level Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

46 Mean Cost Per Aircraft Resolution Results ε = 1 ε = 2 ε = 3 mean cost per aircraft number of aircraft Mean cost per aircraft found by the MA with 300 s time limit Constrainedness/tightness: density of trajectories increases w.r.t. number of aircraft and uncertainty level in a fixed airspace volume As expected, more costly maneuvers needed to satisfy all constraints Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

47 Resolution Results Convergence of the Memetic Algorithm maneuvers cost maneuvers cost number of remaining conflicts number of remaining conflicts time (s) Cost and conflicts w.r.t. elapsed time for 100 aircraft and ε = 3 First: number of conflicts decreased until feasible Second: maneuvers cost improved while maintaining feasibility MA efficient enough on instances comparable to real-life scenarios Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

48 Conclusion Conclusion and Further Work Conclusions 3D extension of the en-route conflict resolution framework [ATM 2013] In-between waypoints with more complete uncertainty model New Memetic Algorithm with outstanding results llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

49 Conclusion Conclusion and Further Work Conclusions 3D extension of the en-route conflict resolution framework [ATM 2013] In-between waypoints with more complete uncertainty model New Memetic Algorithm with outstanding results Further Work Scenarios based on real data with simulated flight plans Embedded resolution: integration into fast-time simulator (CATS) Parallel cooperation of solvers to achieve better performances/proofs llignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19

50 Conclusion Conclusion and Further Work Conclusions 3D extension of the en-route conflict resolution framework [ATM 2013] In-between waypoints with more complete uncertainty model New Memetic Algorithm with outstanding results Further Work Scenarios based on real data with simulated flight plans Embedded resolution: integration into fast-time simulator (CATS) Parallel cooperation of solvers to achieve better performances/proofs GO CHECK YOUR ALGO AT clusters.recherche.enac.fr Allignol, Barnier, Durand, Gondran, Wang (ENAC) 3D En-Route Conflict Resolution ATM / 19