Application of Absorbing Markov Chains to the Assessment of Education Attainment Rates within Air Force Materiel Command Civilian Personnel

Increasing the education levels of an organization is a common response when attempting to improve organizational performance; however, organizational performance improvements are seldom found when the current and future workforce education levels are unknown. In this research, absorbing Markov chains are used to probabilistically forecast the educational composition of the Air Force Materiel Command civilian workforce to enable organizational performance improvements. Through the purposeful decoupling of effects resulting from recent workforce arrivals and education level progressions, this research attempts to determine the implications that stationarity assumptions have throughout the model development process of an absorbing Markov chain. The results of the analysis indicate that the four combinations of stationarity assumptions perform similarly at representing the historical data and that the forecasted educational attainment rates of the Air Force Materiel Command civilian workforce are expected to increase significantly

Stochastic Preemptive Goal Programming to Balance Goal Achievements under Uncertainty

Excerpt: Many decision-making problems contain multiple, competing objectives. As a subfield of multi-criteria decision-making, deterministic goal programming represents one of the most common approaches to combine multiple, competing objectives.Abstract © 2021 SpringerNature