Weapon-target assignment problem: exact and approximate solution algorithms

Alexandre Colaers Andersen, Konstantin Pavlikov and Túlio A. M. Toffolo ()
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Alexandre Colaers Andersen: University of Southern Denmark
Konstantin Pavlikov: University of Southern Denmark
Túlio A. M. Toffolo: Federal University of Ouro Preto

Annals of Operations Research, 2022, vol. 312, issue 2, No 1, 606 pages

Abstract: Abstract The Weapon-Target Assignment (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 $$2.0\%$$ 2.0 % within 2 h of computational runtime.

Keywords: Branch-and-adjust; Integer programming; Weapon-target assignment problem; Probability chains (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-022-04525-6

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