A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions

被引:7
作者
Karasakal, Orhan [1 ]
Karasakal, Esra [2 ]
Silav, Ahmet [2 ]
机构
[1] Cankaya Univ, Ind Engn Dept, Ankara, Turkey
[2] Middle East Tech Univ, Ind Engn Dept, Ankara, Turkey
关键词
Dynamic weapon target allocation problem; Air defense; Rescheduling; Artificial neural network; DEFENSE;
D O I
10.1007/s00500-021-05923-x
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study, we develop a new solution approach for the dynamic missile allocation problem of a naval task group (TG). The approach considers the rescheduling of the surface-to-air missiles (SAMs), where a set of them have already been scheduled to a set of attacking anti-ship missiles (ASMs). The initial schedule is mostly inexecutable due to disruptions such as neutralization of a target ASM, detecting a new ASM, and breakdown of a SAM system. To handle the dynamic disruptions while keeping efficiency high, we use a bi-objective model that considers the efficiency of SAM systems and the stability of the schedule simultaneously. The rescheduling decision is time-sensitive, and the amount of information to be processed is enormous. Thus, we propose a novel approach that supplements the decision-maker (DM) in choosing a Pareto optimal solution considering two conflicting objectives. The proposed approach uses an artificial neural network (ANN) that includes an adaptive learning algorithm to structure the DM's prior articulated preferences. ANN acts like a DM during the engagement process and chooses one of the non-dominated solutions in each rescheduling time point. We assume that the DM's utility function is consistent with a non-decreasing quasi-concave function, and the cone domination principle is incorporated into the solution procedure. An extensive computational study is provided to present the effectiveness of the proposed approach.
引用
收藏
页码:10153 / 10166
页数:14
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