|Ph.D Student||Tahan Meir|
|Subject||Modeling and Analyses of Engineering Systems Integration|
|Department||Department of Aerospace Engineering||Supervisor||Professor Yoseph Ben-Asher|
|Full Thesis text - in Hebrew|
Integration is one of the main processes in a wider task of the verification and validation of a system’s technical, functional and MMI requirements. Despite intensive preparations the integration process frequently suffers from many unexpected difficulties. One of the reasons for this phenomenon is that fairly high uncertainties are involved in the integration process.
The integration process takes place at the end of the system development. The integration process must be as efficient as possible in order to cover for any deviations created in earlier development stages so that the project can be completed on time and within the given budget. The question then is: what kind of approach is required for an optimal integration process? And what kind of approach is required for the whole project?
The research goal was to determine what kind of approach is required for a given project to perform an optimal integration process and what kind of approach is required for the whole project. The optimization characteristics are effort and time. The research performed in three steps: The first step goal was to examine the determination that incremental integration approach is better than the “big-bang” integration approach. The second step was to find an optimization methodology for the integration process assuming that all the integration parameter are fix, known and deterministic. This model is used for finding what kind of approach is required for a given project in order to perform an optimal integration process, and what kind of approach is required for the whole project. The third step was to upgrade the model for handling uncertainties and to find the best approach for the optimal integration and for the optimal project.
An integration model is developed and the use of dynamic programming as an optimization tool is employed. The uncertainties involved with incremental integration process are handled by three basic approaches according to the uncertainty/knowledge level: statistical analysis under the assumption that the mean and variance of the underlying distributions are known (lowest uncertainty level), a Monte Carlo based method when the uncertain values can be bounded from below and above by known quantities, and information gap theory when only nominal values are given.
The integration model is demonstrated by analyzing an integration case of a small tactical missile. The model produced different strategies for cost and project duration. Applying the uncertainty model produced different strategies as well.