Airspace sectorization with constraints

Huy Trandac; Philippe Baptiste; Vu Duong

RAIRO - Operations Research (2010)

  • Volume: 39, Issue: 2, page 105-122
  • ISSN: 0399-0559

Abstract

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We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems.

How to cite

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Trandac, Huy, Baptiste, Philippe, and Duong, Vu. "Airspace sectorization with constraints." RAIRO - Operations Research 39.2 (2010): 105-122. <http://eudml.org/doc/105324>.

@article{Trandac2010,
abstract = { We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems. },
author = {Trandac, Huy, Baptiste, Philippe, Duong, Vu},
journal = {RAIRO - Operations Research},
keywords = {Airspace sectorization; constraint programming.; airspace sectorization; constraint programming},
language = {eng},
month = {3},
number = {2},
pages = {105-122},
publisher = {EDP Sciences},
title = {Airspace sectorization with constraints},
url = {http://eudml.org/doc/105324},
volume = {39},
year = {2010},
}

TY - JOUR
AU - Trandac, Huy
AU - Baptiste, Philippe
AU - Duong, Vu
TI - Airspace sectorization with constraints
JO - RAIRO - Operations Research
DA - 2010/3//
PB - EDP Sciences
VL - 39
IS - 2
SP - 105
EP - 122
AB - We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems.
LA - eng
KW - Airspace sectorization; constraint programming.; airspace sectorization; constraint programming
UR - http://eudml.org/doc/105324
ER -

References

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  1. S. Barbara, A tutorial on constraint programming. Technical Report 95.14, School of Computer Studies, University of Leeds (1995).  
  2. S.T. Barnard and H.D. Simon, A fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems, in Proc. of The sixth SIAM conference on Parallel Processing for Scientific Computing (1993) 711–718.  
  3. R. Bartak, Online guide to constraint programming, (1998).  URIhttp://ktilinux.ms.mff.cuni.cz/~bartak/constraints/
  4. Y. Caseau, F.X. Josset and F. Laburthe, CLAIRE: Combining sets, search and rules to better express algorithms, in Proc. of the 16th International Conference on Logic Programming, Las Cruces, New Mexico – USA, Nov.–Dec. (1999).  
  5. J. Chen, Computational geometry: Methods and applications. Department Computer Science, Texas A&M University (February 1996).  
  6. A. Colmerauer, An introduction to PROLOG-III. Commun. ACM33 (1990) 69–90.  
  7. A. Colmerauer, Les bases de Prolog IV. Technical report, Laboratoire d'Informatique de Marseille (1996).  
  8. D. Delahaye, Optimisation de la sectorisation de l'espace aérien par algorithmes génétiques. Ph.D. Thesis, École Nationale Supérieure de l'Aéronautique et de l'Espace (1995).  
  9. D. Delahaye, J.M. Alliot, M. Schoenauer and J.L. Farges, Genetic algorithms for automatic regrouping of air traffic control sectors, in Proc. of the Fourth International Conference on Evolutionary Programming, MIT Press (March 1995) 657–672.  
  10. D. Delahaye, M. Schoenauer and J.M. Alliot, Airspace sectoring by evolutionary computation, in IEEE International Congress on Evolutionary Computation (1998).  
  11. C.M. Fiduccia and R.M. Mattheyses, A linear time heuristic for improving network partitions, in Proc. of 19th ACM/IEEE Design Automation Conference (1982) 175–181.  
  12. P.O. Fjallstrom, Algorithms for graph partitioning: A survey. Linkoping Electronic Articles in Computer and Information Science3 (1998).  
  13. M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman & Co. (1979).  
  14. J.R. Gilbert, G.L. Miller and S.H. Teng, Geometric mesh partitioning: Implementation and experiments. SIAM J. Sci. Comput.19 (1998) 2091–2110.  
  15. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley (1989).  
  16. B. Hendrickson and R. Leland, An improved spectral graph partitioning algorithm for mapping parallel computations. SIAM J. Sci. Comput.16 (1995) 452–469.  
  17. B. Hendrickson and R. Leland, A multilevel algorithm for partitioning graphs, in Proc. of the 1995 ACM/IEEE Conference on Supercomputing, San Diego, California, USA, ACM Press (1995).  
  18. G. Karypis and V. Kumar, A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput.20 (1998) 359–392.  
  19. G. Karypis and V. Kumar, Multilevel k-way partitioning scheme for irregular graphs. Journal of Parallel and Distributed Computing48 (1998) 96–129.  
  20. B.W. Kernighan and S. Lin, An efficient heuristic procedure for partitionning graphs. BELL System Technical Journal (February 1970) 291–307.  
  21. F. Laburthe, CHOCO – A Constraint Programming kernel for solving combinatorial optimization problems (2002).  
  22. F. Laburthe and the OCRE group, CHOCO: Implementing a CP kernel, in CP00 Post Conference Workshop on Techniques for Implementing Constraint programming Systems (TRICS), Singapore (2000).  
  23. S. Manuel, M.L. José, M.B. Victor and M.R. José, Genes: a genetic algorithms and fast time simulation, in 3rd ATM R&D Symposium, Spain (2002).  
  24. A. Okabe et al., Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. New York, Wiley (1992).  
  25. J.-F. Puget, A C++ implementation of CLP, in Proc. of the Second Singapore International Conference on Intelligent Systems, Singapore (1994).  
  26. R.E. Tarjan, Depth-first search and linear graph algorithms. SIAM J. Comput.1 (1972) 146–160.  
  27. M. Wallace, S. Novello and J. Schimpf, Eclipse: A platform for constraint logic programming. Technical report, IC-Parc, Imperial College, London (1997).  

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