SUMMARY

Historically, aerospace development programs have frequently been marked by performance
shortfalls, cost growth, and schedule slippage. New technologies included
in systems are considered to be one of the major sources of this programmatic risk.
Decisions regarding the choice of technologies to include in a design are therefore
crucial for a successful development program. This problem of technology selection
is a challenging exercise in multi-objective decision making. The complexity of this
selection problem is compounded by the geometric growth of the combinatorial space
with the number of technologies being considered and the uncertainties inherent in
the knowledge of the technological attributes. These problems are not typically addressed
in the selection methods employed in common practice. Consequently, a
method is desired to aid the selection of technologies for complex systems design with
consideration of the combinatorial complexity, multi-dimensionality, and the presence
of uncertainties.

Several categories of techniques are explored to address the shortcomings of current
approaches and to realize the goal of an efficient and effective combinatorial
technology space exploration method. For the multi-objective decision making, a posteriori
preference articulation is implemented. To realize this, a stochastic algorithm
for Pareto optimization is formulated based on the concepts of SPEA2. Techniques
to address the uncertain nature of technology impact on the system are also examined.
Monte Carlo simulations using the surrogate models are used for uncertainty
quantification. The concepts of graph theory are used for modeling and analyzing
compatibility constraints among technologies and assessing their impact on the technology
combinatorial space. The overall decision making approach is enabled by the
application of an uncertainty quantification technique under the framework of an
efficient probabilistic Pareto optimization algorithm. As a result, multiple Pareto
hyper-surfaces are obtained in a multi-dimensional objective space. Each hypersurface
represents a specified probability level, which in turn enables probabilistic
comparison of various options. Other more traditional technology selection and scanning
techniques such as the greedy algorithm, one-on one-off technique and designs of
experiments are also explored. An advisor to recommend the best selection technique
from amongst these options based on the complexity and scope of the problem is also
an important contribution of this research.

Various techniques used for creating the exploration and decision making methodology
are experimented on a benchmark knapsack problem. These techniques are
used in a synergistic manner to formulate the Pareto Optimization and Selection of
Technologies (POST) methodology. POST is implemented on an example technology
exploration and selection problem for a 300 passenger commercial aircraft. This is
a large problem with 29 technologies, 11 objectives and 4 constraints. Initially, the
technologies and their system impacts are defined along with their uncertainties. The
computational complexity is evaluated and the problem dimensionality reduced using
a dominance structure preserving approach. Probabilistic Pareto optimization is implemented
with the reduced dimensionality and three Pareto layers each corresponding
to a predefined probability level are created. These Pareto layers are exported to
a visualization and analysis environment enabled by JMPr. The technology combinations
on these Pareto layers are explored using various visualization tools and one
combination is selected. The main outcome of this research is a method based on a
consistent analytical foundation to create a dynamic tradeoff environment in which
decision makers can interactively explore and select technology combinations.