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This thesis provides a stochastic modeling tool to assist in the component selection process for Army Aviation's Condition-Based Maintenance Plus (CBM+) program. This work is in conjunction with the Operations Research Center of Excellence (ORCEN) at the United States Military Academy to assist in providing insight for the U.S. Aviation and Missile Command (AMCOM). The component selected for this thesis is the AH-64/UH-60 T701C Turbine Helicopter Engine. Data analysis of the failure data indicated that a nonhomogeneous Poisson process appropriately modeled the failure characteristics of this engine. A Microsoft Excel simulation utilizing Crystal Ball version 5.5 compares an engine monitored by CBM+ versus the traditional Legacy system of maintenance. This simulation provides information on diagnosed faults, mission aborts, repair times, false positives, and logistical implications. This simulation is generic and can be used in comparing CBM+ candidate components for future inclusion into the CBM+ program. Results suggest when considering a component for inclusion in the CBM+ program important factors to consider are even the smallest false positive rate can invalidate process, large sensor probability of detection isn't necessary for beneficial results, and by entering a component into the CBM+ the on hand component requirements can be greatly reduced. / US Army (USA) author.

Ph.D.

A dissertation submitted to the Faculty of Arts, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Arts. / Real-life decision-making problems can often be modelled by mathematical programs (or optimization models). It is common for there to be uncertainty about the parameters of such optimization models. Usually, this uncertainty is ignored and a simplified deterministic program is obtained. Stochastic programs take account of this uncertainty by including a probabilistic description of the uncertain parameters in the model. Stochastic programs are therefore more appropriate or valuable than deterministic programs in many situations, and this is emphasized throughout the dissertation. The dissertation contains a development of the theory of stochastic programming, and a number of illustrative examples are formulated and solved. As a real-life application, a stochastic model for the unit commitment problem facing Eskom (one of the world's largest producers of electricity) is formulated and solved, and the solution is compared with that of the current strategy employed by Eskom. / AC 2018

by Lau Chung Kei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1980. / Bibliography: leaf 90.