MAAP: The military aircraft allocation planner

P. Abrahams; R. Balart; J. S. Byrnes; Douglas Cochran; M. J. Larkin; W. Moran; G. Ostheimer; A. Pollington

MAAP: The military aircraft allocation planner

P. Abrahams, R. Balart, J. S. Byrnes, Douglas Cochran, M. J. Larkin, W. Moran, G. Ostheimer, A. Pollington

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present an application of Genetic Algorithms to the field of large-scale allocation problems in which a collection of resources (assets) must be mapped in an optimal or near-optimal manner to a number of objectives (targets), as measured by an objective function. Such problems are complex due to their requirements for integer solutions, non-linear objective functions and linear asset constraints. Genetic Algorithms have turned out to be a natural fit for this application. In this paper, we summarize the scope of the MAAP tool prototype as delivered to the U.S. Air Force and indicate our plans for ongoing and future research.

Original language	English (US)
Title of host publication	Proceedings of the IEEE Conference on Evolutionary Computation, ICEC
Editors	Anon
Publisher	IEEE
Pages	336-341
Number of pages	6
State	Published - 1998
Externally published	Yes
Event	Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC'98 - Anchorage, AK, USA Duration: May 4 1998 → May 9 1998

Other

Other	Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC'98
City	Anchorage, AK, USA
Period	5/4/98 → 5/9/98

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

Cite this

Abrahams, P, Balart, R, Byrnes, JS, Cochran, D, Larkin, MJ, Moran, W, Ostheimer, G & Pollington, A 1998, MAAP: The military aircraft allocation planner. in Anon (ed.), Proceedings of the IEEE Conference on Evolutionary Computation, ICEC. IEEE, pp. 336-341, Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC'98, Anchorage, AK, USA, 5/4/98.

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title = "MAAP: The military aircraft allocation planner",

abstract = "We present an application of Genetic Algorithms to the field of large-scale allocation problems in which a collection of resources (assets) must be mapped in an optimal or near-optimal manner to a number of objectives (targets), as measured by an objective function. Such problems are complex due to their requirements for integer solutions, non-linear objective functions and linear asset constraints. Genetic Algorithms have turned out to be a natural fit for this application. In this paper, we summarize the scope of the MAAP tool prototype as delivered to the U.S. Air Force and indicate our plans for ongoing and future research.",

author = "P. Abrahams and R. Balart and Byrnes, {J. S.} and Douglas Cochran and Larkin, {M. J.} and W. Moran and G. Ostheimer and A. Pollington",

year = "1998",

language = "English (US)",

pages = "336--341",

editor = "Anon",

booktitle = "Proceedings of the IEEE Conference on Evolutionary Computation, ICEC",

publisher = "IEEE",

note = "Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC'98 ; Conference date: 04-05-1998 Through 09-05-1998",

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T2 - Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC'98

AU - Abrahams, P.

AU - Balart, R.

AU - Byrnes, J. S.

AU - Cochran, Douglas

AU - Larkin, M. J.

AU - Moran, W.

AU - Ostheimer, G.

AU - Pollington, A.

PY - 1998

Y1 - 1998

N2 - We present an application of Genetic Algorithms to the field of large-scale allocation problems in which a collection of resources (assets) must be mapped in an optimal or near-optimal manner to a number of objectives (targets), as measured by an objective function. Such problems are complex due to their requirements for integer solutions, non-linear objective functions and linear asset constraints. Genetic Algorithms have turned out to be a natural fit for this application. In this paper, we summarize the scope of the MAAP tool prototype as delivered to the U.S. Air Force and indicate our plans for ongoing and future research.

AB - We present an application of Genetic Algorithms to the field of large-scale allocation problems in which a collection of resources (assets) must be mapped in an optimal or near-optimal manner to a number of objectives (targets), as measured by an objective function. Such problems are complex due to their requirements for integer solutions, non-linear objective functions and linear asset constraints. Genetic Algorithms have turned out to be a natural fit for this application. In this paper, we summarize the scope of the MAAP tool prototype as delivered to the U.S. Air Force and indicate our plans for ongoing and future research.

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M3 - Conference contribution

AN - SCOPUS:0031678418

SP - 336

EP - 341

BT - Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

A2 - Anon, null

PB - IEEE

Y2 - 4 May 1998 through 9 May 1998

ER -

MAAP: The military aircraft allocation planner

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