Architecting systems for optimal lifetime adaptability

System architecture decisions such as the assignment of components to modules can have a large impact on the system's lifetime adaptability and cost. We broaden systems architecting theory by considering components' option values and interface costs when making the assignment decision. We propose an analytical model to identify the trade-offs between an inexpensive but less adaptable system and an expensive but adaptable one. We demonstrate the model with a realistic example of an Unmanned Air Vehicle (UAV) and use a genetic algorithm to identify an architecture that optimally balances cost and adaptability. Finally, we compensate variations stemming from uncertainties in the input data by means of sensitivity analysis, depicting optimal architectures via lattice charts. By way of example, we demonstrate that optimization provides considerably more cost effective lifetime architectures. In addition, conducting sensitivity analysis combined with lattice charts enable the selection of significantly more robust architectures when the input data is inherently imprecise. The approach received preliminary validation in several real industrial pilot cases.