packages (Long et al. 1999, Stamatopoulos et al. 2003,
EUROCONTROL 2001) that perform both capacity
and delay analyses and, in the instance of the last
two references, include models of aprons and aircraft
stands. After some necessary enhancements and an
adequate amount of testing in a variety of airport
environments, packages of this type may provide airport
planners within a few years with an easy-to-use
and very fast set of tools for the study of a host of
airside issues.
4.1.2. Airside Simulations. General-purpose simulation
models of airside operations first became
viable in the early 1980s and have been vested with
increasingly sophisticated features since then. Three
models currently dominate this field internationally:
SIMMOD, The Airport Machine, and the Total Airport
and Airspace Modeler (TAAM). A report by
Odoni et al. (1997) contains detailed reviews (somewhat
out-of-date by now) of these and several other
airport and airspace simulation models and assesses
the strengths and weaknesses of each. At their current
state of development (and in the hands of expert
users), they can be powerful tools in studying detailed
airside design issues, such as figuring out the best
way to remove an airside bottleneck or estimating the
amount by which the capacity of an airport is reduced
due to the crossing of active runways by taxiing
aircraft.
Unfortunately, these models are frequently misused
in practice, at great cost to the client organization.
This happens when they are applied to the study
of “macroscopic” issues that can have only approximate
answers because of the uncertainty inherent in
the input data. An example is a question that often
confronts airport operators: When will airside delays
reach a level that will require a major expansion of an
airport’s capacity (e.g., through the construction of a
new runway)? Questions of this type, often requiring
a look far into the future, are best answered through
the approximate analytical models surveyed earlier,
which permit easy exploration of a large number of
alternative scenarios and hypotheses. Detailed simulation
models, by contrast, cannot cope well with the
massive uncertainty involved because they require
inputs that are difficult to produce (e.g., a detailed
schedule of aircraft movements at the airport for a
typical day 10 or 15 years hence) and lack credibility
under the circumstances.
4.1.3. Optimizing Airside Operations. The airside
models discussed so far are descriptive in nature.
Their objective is to help users understand and predict
the operational characteristics of the various
airside facilities under different operating scenarios.
A considerable amount of OR work with an optimization
focus also exists, much of it concerned with the
effective use of runway systems.
The capacity of a runway is largely determined
by the separation requirements specified by the
providers of ATM services (e.g., the FAA in the United
States). For any pair of consecutive runway operations
these requirements depend on the type of aircraft
involved. For example, in the United States,
when an arriving “heavy” (H) aircraft—defined as
one with a maximum takeoff weight (MTOW) greater
than 255,000 lbs—is immediately followed by an arriving
“small” (S) aircraft (MTOW < 41,000 lbs), the
required separation between them, at the instant
when H is about to touch down on the runway, is
6 nautical miles (∼10.9 km). This is because “heavy”
aircraft (wide-body jets) may generate severe wake
turbulence, which may be hazardous to other aircraft
behind it. By contrast, when an aircraft of type Sis
followed by one of type H, the required separation is
2.5 nautical miles (∼4.5 km). Note that given a number
n of aircraft, all waiting to land on a runway,
the problem of determining the sequence of landings,
such that the time when the last aircraft lands is minimized,
is a Hamiltonian path problem with n points.
However, this is only a static version of a problem
which in truth is a dynamic one: Over time the
pool of aircraft available to land changes, as some
aircraft reach the runway while new aircraft join
the arrivals queue. Moreover, minimizing the “latest
landing time” (or maximizing “throughput”) should
not necessarily be the objective of optimal sequencing.
Many alternative objective functions, such as minimizing
the average waiting time per passenger, are
just as reasonable. A further complication is that the
very idea of “sequencing” runs counter to the traditional
adherence of ATM systems to a first-come,
first-served (FCFS) discipline. Deviations from FCFS
raise concerns among some airside users about the