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Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft (Paperback)
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Decision-Aiding and Optimization for Vertical Navigation of Long-Haul Aircraft (Paperback)
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Most decisions made in the cockpit are related to safety, and have
therefore been proceduralized in order to reduce risk. There are
very few which are made on the basis of a value metric such as
economic cost. One which can be shown to be value based, however,
is the selection of a flight profile. Fuel consumption and flight
time both have a substantial effect on aircraft operating cost, but
they cannot be minimized simultaneously. In addition, winds,
turbulence, and performance vary widely with altitude and time.
These factors make it important and difficult for pilots to (a)
evaluate the outcomes associated with a particular trajectory
before it is flown and (b) decide among possible trajectories. The
two elements of this problem considered here are: (1) determining
what constitutes optimality, and (2) finding optimal trajectories.
Pilots and dispatchers from major u.s. airlines were surveyed to
determine which attributes of the outcome of a flight they
considered the most important. Avoiding turbulence-for passenger
comfort-topped the list of items which were not safety related.
Pilots' decision making about the selection of flight profile on
the basis of flight time, fuel burn, and exposure to turbulence was
then observed. Of the several behavioral and prescriptive decision
models invoked to explain the pilots' choices, utility maximization
is shown to best reproduce the pilots' decisions. After considering
more traditional methods for optimizing trajectories, a novel
method is developed using a genetic algorithm (GA) operating on a
discrete representation of the trajectory search space. The
representation is a sequence of command altitudes, and was chosen
to be compatible with the constraints imposed by Air Traffic
Control, and with the training given to pilots. Since trajectory
evaluation for the GA is performed holistically, a wide class of
objective functions can be optimized easily.
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