The weapon-targetassignment (WTA) problem aims to assign a set of weapons to a number of assets (targets), such that the expected value of survived targets is minimized. The WTA problem is a nonlinear combinatorial o...
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The weapon-targetassignment (WTA) problem aims to assign a set of weapons to a number of assets (targets), such that the expected value of survived targets is minimized. The WTA problem is a nonlinear combinatorial optimization problem known to be NP-hard. This paper applies several existing techniques to linearize the WTA problem. One linearization technique (Camm et al. in Oper Res 50(6):946-955, 2002) approximates the nonlinear terms of the WTA problem via convex piecewise linear functions and provides heuristic solutions to the WTA problem. Such approximation problems are, though, relatively easy to solve from the computational point of view even for large-scale problem instances. Another approach proposed by O' Hanley et al. (Eur J Oper Res 230(1):63-75, 2013) linearizes the WTA problem exactly at the expense of incorporating a significant number of additional variables and constraints, which makes many large-scale problem instances intractable. Motivated by the results of computational experiments with these existing solution approaches, a specialized new exact solution approach is developed, which is called branch-and-adjust. The proposed solution approach involves the compact piecewise linear convex under-approximation of the WTA objective function and solves the WTA problem exactly. The algorithm builds on top of any existing branch-and-cut or branch-and-bound algorithm and can be implemented using the tools provided by state-of-the-art mixed integer linear programming solvers. Numerical experiments demonstrate that the proposed specialized algorithm is capable of handling very large scale problem instances with up to 1500 weapons and 1000 targets, obtaining solutions with optimality gaps of up to 2.0% within 2 h of computational runtime.
In the present research, we are going to obtain the solution of the weapon-targetassignment (WTA) problem. According to our search in the scientific reported papers, this is the first scientific attempt for resolving...
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In the present research, we are going to obtain the solution of the weapon-targetassignment (WTA) problem. According to our search in the scientific reported papers, this is the first scientific attempt for resolving of WTA problem by projection recurrent neural network (RNN) models. Here, by reformulating the original problem to an unconstrained problem a projection RNN model as a high-performance tool to provide the solution of the problem is proposed. In continuous, the global exponential stability of the system was proved in this research. In the final step, some numerical examples are presented to depict the performance and the feasibility of the method. Reported results were compared with some other published papers.
The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapontargetassignmentproblem. As many solution techniques are based upon a transformati...
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The problem of targeting and engaging individual missiles (targets) with an arsenal of interceptors (weapons) is known as the weapontargetassignmentproblem. As many solution techniques are based upon a transformation of the objective function, their final solutions rarely produce optimal solutions. We propose a nonlinear branch and bound algorithm to provide the first optimization approach to the untransformed problem found in the literature. Further, we propose a new heuristic based upon the branch and bound algorithm which dominates other heuristics explored in optimality gap. We also propose a heuristic based upon the optimal solution to the quiz problem which finds solutions within 6% of optimal for small problems and provides statistically similar results as one of the best heuristics found in the literature for larger problems while solving these problems in ten thousandths of the time.
Identifying an adversary's strategic goals and values requires deliberate and unbiased analysis. This research is motivated by the premise that, if one observes an adversary's actions or planned actions, it is...
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Identifying an adversary's strategic goals and values requires deliberate and unbiased analysis. This research is motivated by the premise that, if one observes an adversary's actions or planned actions, it is possible to draw reasonable inferences about their values, thereby reducing misperceptions and informing better decisions. Within the context of the static weapontargetassignmentproblem, this research develops and empirically compares alternative methods to rationalize an adversary's value hierarchy over targets that informs their observed decisions. Such methods either identify the extreme points of a polytope within a unit simplex of relative target values that encompasses all possible relative target values based on a weak dominance criterion or a subset of points within the polytope. This research characterizes the solution methods' practical tractability for use on larger-sized problems and their generalizability to other problems. Even for the superlative technique examined, testing illustrates the computationally challenging nature of identifying the defining polytope of relative target values, and the work concludes with suggestions for metaheuristic technique development. (C) 2021 Elsevier Ltd. All rights reserved.
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