Asset-task assignment algorithms in the presence of execution uncertainty

Computer Journal

Volumn 54, Issue 9, 2011, Pages 1514-1525

  Timotheou, Stelios ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

execution uncertainty; lower bounds; network flow problems; nonlinear combinatorial optimization; optimum asset assignment; piecewise linear approximation; random neural networks

Indexed keywords

EXECUTION UNCERTAINTIES; LOWER BOUNDS; NETWORK FLOW PROBLEMS; NONLINEAR COMBINATORIAL OPTIMIZATION; OPTIMUM ASSET ASSIGNMENT; PIECEWISE LINEAR APPROXIMATIONS; RANDOM NEURAL NETWORK;

ALGORITHMS; COMBINATORIAL OPTIMIZATION; COST ACCOUNTING; COSTS; OPTIMIZATION; PIECEWISE LINEAR TECHNIQUES;

NEURAL NETWORKS;

EID: 80052575882     PISSN: 00104620     EISSN: 14602067     Source Type: Journal    
DOI: 10.1093/comjnl/bxq063     Document Type: Article

References (31)

1. Burkard, R.E. and Cela, E. (1999) Linear Assignment Problems and Extensions. In Pardalos, P. and Du, D.-Z. (eds), Handbook of Combinatorial Optimization-Supplement Volume A, pp. 75-149. Kluwer Academic Publishers.
2. Marden, J.R. and Wierman, A. Distributed Welfare Games with Applications to Sensor Coverage. Proc. 47th IEEE Conf. Decision and Control, Cancún, México.
3. Esen, O., Cetin, E. and Esen, S.T. (2008) A mathematical immunochemoradiotherapy model: A multiobjective approach. Nonlinear Anal. Real World Appl., 9, 511-517.
4. Gelenbe, E., Timotheou, S. and Nicholson, D. (2010). Fast distributed near optimum assignment of assets to tasks. Comput. J., doi:10.1093/comjnl/ bxq010.
5. Pardalos, P. and Pitsoulis, L.S. (2000) Nonlinear Assignment Problems: Algorithms and Applications. Kluwer Academic Publishers, Dordrecht.
6. Lloyd, S.P. and Witsenhausen, H.S. (1986) Weapons Allocation is NP-complete. Proc. 1986 Summer Computer Simulation Conf., Reno, NV, USA, July 28-30, pp. 1054-1058. Society for Computer Simulation.
7. den Broeder, G.G., Ellison, R.E. and Emerling, L. (1959) On optimum target assignments. Oper. Res., 7, 322-326.
8. Kolitz, S.E. (1988) Analysis of a Maximum Marginal Return Assignment Algorithm. Proc. of the 27th IEEE Conf. Decision and Control, Austin, TX, USA, December 7-9, pp. 2431-2436. IEEE, NewYork.
9. Hosein, P.A. (1989) A class of dynamic nonlinear resource allocation problems. Ph.D. Thesis Massachusetts Institute of Technology, Cambridge, MA.
10. Chang, S.-C., James, R.M. and Shaw, J.J. (1987) Assignment Algorithm for Kinetic EnergyWeapons in Boost Phase Defence. Proc. 26th IEEE Conf. Decision and Control, Los Angeles, CA, December 9-11, pp. 1678-1683. IEEE, NewYork.
11. Ahuja, R.K., Kumar, A., Jha, K.C. and Orlin, J.B. (2007) Exact and heuristic algorithms for the weapon-target assignment problem. Oper. Res., 55, 1136-1146.
12. Wacholder, E. (1989) A neural network-based optimization algorithm for the static weapon-target assignment problem. INFORMS J. Comput., 1, 232-246.
13. Yanxia, W., Longjun, Q., Zhi, G. and Lifeng, M. (2008) Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm. J. Syst. Eng. Electron., 19, 939-944.
14. Lee, Z.-J., Lee, C.-Y. and Su, S.-F. (2002) An immunitybased ant colony optimization algorithm for solving weapon-target assignment problem. Appl. Soft Comput., 2, 39-47.
15. Lee, Z.-J., Su, S.-F. and Lee, C.-Y. (2003) Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics. IEEE Trans. Syst. Man Cybern. B, Cybern., 33, 113-121.
16. Loiola, E.M., de Abreu, N.M.M., Boaventura-Netto, P.O., Hahn, P. and Querido, T. (2007) A survey for the quadratic assignment problem. Eur. J. Oper. Res., 176, 657-690.
17. Pitsoulis, L.S. (2009) Quadratic Semi-assignment Problem. In Floudas, C.A. and Pardalos, P.M. (eds), Encyclopedia of Optimization, pp. 3170-3171. Springer.
18. Sahni, S. and Gonzalez, T. (1976) P-complete approximation problems. J. ACM, 23, 555-565.
19. Magirou, V.F. and Milis, J.Z. (1989) An algorithm for the multiprocessor assignment problem. Oper. Res. Lett., 8, 351-356.
20. Malucelli, F. (1996) A polynomially solvable class of quadratic semi-assignment problems. Euro. J. Oper. Res., 91, 619-622.
21. Malucelli, F. and Pretolani, D. (1995) Lower bounds for the quadratic semi-assignment problem. Euro. J. Oper. Res., 83, 365-375.
22. Gelenbe, E. (1989) Random neural networks with negative and positive signals and product form solution. Neural Comput., 1, 502-510.
23. Gelenbe, E. (1990) Stability of the random neural network. Neural Comput., 2, 239-247.
24. Gelenbe, E. (1993) Learning in the recurrent random network. Neural Comput., 5, 154-164.
25. Aguilar, J. and Gelenbe, E. (1997) Task assignment and transaction clustering heuristics for distributed systems. Inf. Sci.-Inf. Comput. Sci., 97, 199-221.
26. Gelenbe, E.,Koubi,V. and Pekergin, F. (1994) Dynamical random neural network approach to the traveling salesman problem. ELEKTRIK, 2, 1-10.
27. Gelenbe, E. and Timotheou, S. (2008) Random neural networks with synchronised interactions. Neural Comput., 20, 2308-2324.
28. Timotheou, S. (2010) The random neural network: A survey. Comput. J., 53, 251-267.
29. Ahuja, R.K., Magnanti, T.L., and Orlin, J.B. (1993) Network Flows: Theory, Algorithms, and Applications. Prentice-Hall, USA.
30. Manne, A. (1958) A target-assignment problem. Oper. Res., 6, 346-351.
31. Murty, K. (1976) Linear and Combinatorial Programming. Krieger Publishing Company, Malabar, FL.