Estimating network characteristics in stochastic activity networks

Article Abstract:

A Monte Carlo method based on the theory of quasi-random points for estimating the distribution functions and means of network completion time and shortest path time in a stochastic activity network is described. The stochastic network problem is presented in detail, characterized through numerical integration, and tackled using crude Monte Carlo methods. The benefits of conditional sampling are then described, the concepts of quasi-random points as they relate to multivariable numerical integration are discussed, and the extent to which known results apply to the problem at hand are shown. Algorithms essential for Monte Carlo network analyses are presented, and a comprehensive sampling plan is described, listing all essential steps in using the cutset approach together with quasi-random points to estimate network quantities of interest.

author: Fishman, George S.
Network analysis (Planning)

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Bootstrapped insights into empirical applications of stochastic dominance

Article Abstract:

Bootstrapping is a statistical method that enhances the understanding and reliability of empirical uses of stochastic dominance. The technique provides data about empirical distribution function (EDF) performance which rekindles the potential empirical usages of stochastic dominance, functioning in a complementary fashion with EDF by: (1) assisting decision makers in finding uncertain order statistics by approximating biases and standard deviations, and (2) smoothing order statistics to bring about a pronounced increase in algorithm power in dominance-evident populations. Bootstrapping as a feasible alternative to the EDF is verified by its power in cumulative density function analyses.

author: Nelson, Ray D., Pope, Rulon D.
Research, Stochastic analysis, Distribution (Probability theory)

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Output decision under demand uncertainty with stochastic production function: a contingent claims approach

Article Abstract:

A contingent claims analysis is presented for the output decisions of a firm facing uncertainty of demand and production technology. The analysis indicates that the optimal output level rises with the higher interest rate under uncertainty, that demand volatility and production lead time can have either a positive or a negative impact on the optimal output level, that increased demand volatility decreases the optimal project value, and that the optimal project value declines with the longer production lead time and the higher interest rate under demand uncertainty.

author: Chung, Kee H.
Management science, Models, Production functions (Economics), Uncertainty (Information theory)

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subjects list: Methods, Analysis, Usage, Stochastic processes, Monte Carlo method, Monte Carlo methods
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