A. Azaron , S.m. Fatemi Ghomi,
Volume 18, Issue 3 (11-2007)
Abstract
Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in accordance with the independent semi-Markov processes over the planning horizon. By using the stochastic dynamic programming, we find a dynamic path with maximum expected length from the source node to the sink node of the stochastic dynamic network. The expected value of such path can be considered as an approximation for the mean project completion time in the original dynamic PERT network.
Luis Ceferino Franco-Arbeláez, Luis Eduardo Franco-Ceballos, Héctor Alonso Olivares-Aguayo,
Volume 34, Issue 1 (3-2023)
Abstract
Previous work has highlighted the need to apply stochastic modeling to understand the dynamics of phenomena occurring in the insurance industry. In this paper, for life insurance and applying a stochastic approach under efficient markets, we use survival probabilities and stochastic differential equations to model the actuarial reserve, changes in the constituted actuarial reserve, and estimated income over time. We present an application, sensitivity analysis, and discussion of the results using United States life tables.
