Improving Defensive Air Battle Management by Solving a Stochastic Dynamic Assignment Problem via Approximate Dynamic Programming

•Military air battle managers must make a sequence of tasking and routing decisions•A high-value asset must be defended from arriving salvos of cruise missiles•An air battle management problem is solved via approximate dynamic programming•Our modeling & solution procedures improve behavior of de...

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Vydáno v:	European journal of operational research Ročník 305; číslo 3; s. 1435 - 1449
Hlavní autoři:	Liles IV, Joseph M., Robbins, Matthew J., Lunday, Brian J.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 16.03.2023
Témata:	air battle management approximate dynamic programming dynamic assignment problem Markov decision process OR in defense Markov decision process approximate dynamic programming OR in defense air battle management dynamic assignment problem
ISSN:	0377-2217, 1872-6860
On-line přístup:	Získat plný text
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Shrnutí:	•Military air battle managers must make a sequence of tasking and routing decisions•A high-value asset must be defended from arriving salvos of cruise missiles•An air battle management problem is solved via approximate dynamic programming•Our modeling & solution procedures improve behavior of defensive counter-air forces•Trajectory-following state sampling and regularization are key algorithm features Military air battle managers face several challenges when directing operations during quickly evolving combat scenarios. These scenarios require rapid assignment decisions to engage moving targets having dynamic flight paths. In defensive operations, the success of a sequence of air battle management decisions is reflected by the friendly force’s ability to maintain air superiority and defend friendly assets. We develop a Markov decision process (MDP) model of a stochastic dynamic assignment problem, named the Air Battle Management Problem (ABMP), wherein a set of unmanned combat aerial vehicles (UCAV) must defend an asset from cruise missiles arriving stochastically over time. Attaining an exact solution using traditional dynamic programming techniques is computationally intractable. Hence, we utilize an approximate dynamic programming (ADP) technique known as approximate policy iteration with least squares temporal differences (API-LSTD) learning to find high-quality solutions to the ABMP. We create a simulation environment in conjunction with a generic yet representative combat scenario to illustrate how the ADP solution compares in quality to a reasonable, closest-intercept benchmark policy. Our API-LSTD policy improves mean success rate by 2.8% compared to the benchmark policy and offers an 81.7% increase in the frequency with which the policy performs perfectly. Moreover, we find the increased success rate of the ADP policy is, on average, equivalent to the success rate attained by the benchmark policy when using a 20% faster UCAV. These results inform military force management and defense acquisition decisions and aid in the development of more effective tactics, techniques, and procedures.
ISSN:	0377-2217 1872-6860
DOI:	10.1016/j.ejor.2022.06.031