Stochastic analysis of combat models under different termination decision rules

Article Abstract:

The stochastic theory of combat was investigated. Specifically, the warfare between two opposing parties with different termination decision rules was characterized as a continuous-time discrete-state space Markov process to obtain characteristics of interest. These include the distribution of combat duration, the probability of victory of either side, the expected survivors and the ratio of expected losses. The analysis was carried out via stochastic Lanchester expressions of modern warfare, area warfare and warfare with smart weapons.

Models, War, Markov processes, Combat, Conflict termination (Military science)

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Stochastic annealing for synthesis under uncertainty

Article Abstract:

The concept of stochastic annealing for optimizing the expected cost of the objective function of synthesis problems in the presence of uncertainty is introduced. The idea, which is based on simulated annealing and stochastic modeling, selects both design variables and the number of samples such that the latter may be varied as the optimum becomes nearer. Its application to a stochastic combinatorial optimization problem, the design synthesis of a Brayton cycle space nuclear power plant, is also discussed.

Combinatorial optimization, Uncertainty, Simulated annealing (Mathematics)

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A new approach to stochastic programming problems: discrete model

Article Abstract:

A heuristic algorithm for solving stochastic programming problems (SPP) with discrete random coefficients is introduced. The approach, which arose from a comparison with the problem of finding the center of gravity of certain physical systems, monitors the heuristic parameters within a plan-and-execute framework such that a 100% reliable solution is attained. The algorithm may also be used for SPPs with probabilistic constraints.

Heuristic, Center of mass, Heuristics (Psychology)

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