A branch‐and‐price algorithm for a targeting problem

IEEE Trans. Syst., Man, Cybern. A

Peng

et al. 2011

Abstract-In this paper, we propose an efficient rule-based heuristic to solve asset-based dynamic weapon-target assignment (DWTA) problems. The main idea of the proposed heuristic is to utilize the domain knowledge of DWTA problems to directly achieve weapon assignment, without large number of function evaluations. We update the saturation states of constraints in the assignment process to guarantee the feasibility of generated solutions. For the purpose of testing the performance of the proposed heuristic, we build a general Monte Carlo simulation-based DWTA framework. For comparison, we also employ a Monte Carlo method (MCM) to make DWTA decisions in different defense scenarios. From simulations with DWTA instances under different scales, the heuristic has obvious advantages over the MCM with regard to solution quality and computation time. The proposed method can solve large-scale DWTA problems (e.g., those including 100 weapons, 100 targets, and four defense stages) within only a few seconds.Index Terms-Combinatorial optimization, constraint handling, decision making, dynamic weapon-target assignment (DWTA), heuristic, military operations.

Section: Introductionmentioning

confidence: 99%

“…Besides, some WTA models also take into account the cost of weapons. For examples, Kwon et al [19] employed the overall firing cost as the objective function for optimization. Interested readers can find a more complicated model which considers the function of special assets in the work of Hosein et al [20].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Rule-Based Constructive Heuristic to Solve Dynamic Weapon-Target Assignment Problem

IEEE Trans. Syst., Man, Cybern. A

Peng

et al. 2011

“…Besides, the cost of weapons is also taken into account in some models like that in ref. [20]. A more complicated model which considers the function of special assets can be found in ref.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary decision-makings for the dynamic weapon-target assignment problem

Sci. China Ser. F-Inf. Sci.

Peng

et al. 2009

The dynamic weapon-target assignment (DWTA) problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general "virtual" representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.decision-making, dynamic weapon-target assignment (DWTA), military command and control, evolutionary computation, memetic algorithms, constraints handling

“…In fact, the probability model employed by Karasakal [11] and the target-based models are just special cases of the asset-based model proposed by Hosein and Athans [6]. Besides, the cost of weapons is also taken into account in some models, like that in the research of Kwon et al [22]. A more complicated model that considers the function of special assets can be found in [23].…”

Section: Introductionmentioning

confidence: 99%

“…Previous researches on WTA mainly focus on SWTA [9]- [22]. In respect of SWTA models, Hosein and Athans [6] proposed an asset-based SWTA model that was also used in [9] and [10].…”

Section: Introductionmentioning

confidence: 99%

Efficient Decision Makings for Dynamic Weapon-Target Assignment by Virtual Permutation and Tabu Search Heuristics

IEEE Trans. Syst., Man, Cybern. C

Zhang

et al. 2010

Abstract-The dynamic weapon-target assignment (DWTA) problem is a typical constrained combinatorial optimization problem with the objective of maximizing the total value of surviving assets threatened by hostile targets through all defense stages. A generic asset-based DWTA model is established, especially for the warfare scenario of force coordination, to formulate this problem. Four categories of constraints, involving capability constraints, strategy constraints, resource constraints (i.e., ammunition constraints), and engagement feasibility constraints, are taken into account in the DWTA model. The concept of virtual permutation (VP) is proposed to facilitate the generation of feasible decisions. A construction procedure (CP) converts VPs into feasible DWTA decisions. With constraint satisfaction guaranteed by the synergy of VPs and the CP, an elaborate local search (LS) operator, namely move-to-head operator, is constructed to avoid repeatedly generating the same decisions. The operator is integrated into two tabu search (TS) algorithms to solve DWTA problems. Comparative experiments involving a random sampling method, an LS method, a hybrid genetic algorithm, a hybrid ant-colony optimization algorithm, and our TS algorithms show that the proposed TS heuristics for DWTA outperform their competitors in most test cases and they are competent for high-quality real-time DWTA decision makings.Index Terms-Combinatorial optimization, constraint handling, Dynamic weapon-target assignment (DWTA), metaheuristics, military decision making, tabu search (TS), virtual permutation (VP).