Assigning Nuclear Weapons with Reactive Tabu Search

Cullenbine, Christopher A.; Gallagher, Mark A.; Moore, James T.

doi:10.5711/morj.8.1.57

Cited by 8 publications

(7 citation statements)

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“…Some problems of interest within this framework include transmission problem in networks (Wieselthier et al, 1992, Cassandras and Julka, 1995, Mishra et al, 2007, spreading code design problem (Krishnamurthy et al, 2004), assignment problem of people evacuation (Francis, 1981), facility locating problem (Ermoliev and Leonardi, 1982), weapons assignment problem (Cullenbine et al, 2003), antenna selection problem (Liu et al, 2012), and, more generally, distributing a discrete amount of resources to a finite number of users in the face of uncertainty (Castanon andWohletz, 2009, Wang andGao, 2010).…”

Section: Motivationmentioning

confidence: 99%

Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Wang

Spall

2011

Proceedings of the 2011 American Control Conference

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Consider the stochastic optimization of a loss function defined on p-dimensional grid of points in Euclidean space. We introduce the middle point discrete simultaneous perturbation stochastic approximation (DSPSA) algorithm for such discrete problems and show that convergence to the minimum is achieved. Consistent with other stochastic approximation methods, this method formally accommodates noisy measurements of the loss function.

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Section: Motivationmentioning

confidence: 99%

Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Wang

Spall

2011

Proceedings of the 2011 American Control Conference

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“…Some consider that deterministic preemptive goal programming is only applicable when the multiple, competing goals have vastly divergent priorities; however, we have found that it works well to ensure that some essential conditions are achieved for a solution to be viable. For instance, the Air Force has successfully applied deterministic preemptive goal programming to problems without vastly divergent priorities in conventional warfare (Gallagher & Kelly, 1991), nuclear warfare (Cullenbine et al, 2003), and budgeting (Nestico et al, 2020). We have also found that effective deterministic preemptive goal formulations define essential goals as high priorities that must be met without reducing the feasible decision space to a single or relatively few points.…”

Section: Introductionmentioning

confidence: 99%

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Ledwith

Hufstetler

Gallagher

2021

Multi Criteria Decision Anal

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Structured decision‐making in the presence of conflicting goals is difficult, and even more so when accounting for uncertainties in the goals or constraints. In this article, we propose a new approach to multi‐criteria decision‐making that extends the deterministic preemptive goal programming approach to account for such uncertainties. The uncertainties may be characterized in various ways including a Bayesian network or extensive Monte Carlo multi‐variate output. We contend the proposed stochastic preemptive goal programming approach is particularly applicable when better goal achievement increases uncertainty or induces risk to other goals. Resulting solutions tend to be a balance of the goals' achievements and robust to minor changes to the goals or constraints. We demonstrate the approach using three illustrative examples: a univariate example, a bivariate example, and a stock portfolio optimization example along with an application to determine military requests for absence. Our preliminary results suggest that a stochastic preemptive goal programming approach represents an effective means of analysing multi‐criteria decision‐making problems under uncertainty.

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“…Hence, our leaders and their analysts segregated studies of conventional and nuclear wars. Most military models only represent conventional forces, such as Gallagher and Kelly, 1 and similarly models of nuclear forces, for example Cullenbine et al, 2 do not include any conventional forces. Even when apparently common terms, such as probability of damage, are used, the definitions of damage are different and incommensurate.…”

Section: Introductionmentioning

confidence: 99%

Modeling a nuclear detonation in a conventional campaign simulation

Hefty

Dickey

Gallagher³

et al. 2015

Journal of Defense Modeling & Simulation

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Military analysts use large simulation models to evaluate potential conventional wars. The proliferation of nuclear weapons has increased the potential for one or a few of them to be employed in a conventional conflict. While complex nuclear effects models exist, their damage levels and measures are not commensurate with what is found in conventional campaign simulations. In this article, we use simple range-to-effects approximations that can be scaled for detonation yield and height of burst (HOB) for the impact of nuclear effects on various combat unit types. We incorporate these relationships in the synthetic theater operations research model (STORM), a widely used conventional campaign simulation. We demonstrate that nuclear detonations can be integrated into campaign simulations and show the impact that a single nuclear weapon may have on conventional force operations.

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Assigning Nuclear Weapons with Reactive Tabu Search

Cited by 8 publications

References 0 publications

Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Discrete simultaneous perturbation stochastic approximation on loss function with noisy measurements

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Modeling a nuclear detonation in a conventional campaign simulation

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