A Highly-Optimized Tolerance (HOT)-Inspired Model of the Large Scale Systems Engineering Process
Large-scale systems engineering efforts involving
multiple stakeholders often have been problematic, and
there has been recent interest in understanding how to
improve the systems engineering process. This paper
presents an approach to modeling the systems
engineering process, with possible extensions to systems
investment and systems operations, inspired by the
highly optimized tolerance (HOT) framework for
understanding complexity in designed systems. HOT is
complementary to agent-based modeling (ABM) in the
sense that it emphasizes the centrally planned aspect of
designed systems with tradeoffs and uncertainty, rather
than distributed decision making based on local
knowledge and goals. To begin the exploration of
models of the systems engineering process, a temporal
model is presented with stakeholder interactions modeled
as random events. Following the HOT approach,
planning behavior is framed as stochastic optimization,
which is reduced to a open-loop control problem. The
initial results suggest promise for the HOT-inspired
framework in helping to understand how to improve the
systems engineering process, but more exploratory work
is needed, including work on relating actual systems
engineering experience to the models.
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