“pre-CDM” models can still be adapted to the CDM
environment, often with little modification. For example,
one of the most critical problems in the planning
of GDPs continues to be the determination of
airport acceptance rates (AAR) for several hours into
the future and in the presence of uncertainty about
airport capacity and air traffic demand. The efficient
stochastic integer program developed for this purpose
by Ball et al. (2003) can be viewed as a direct descendant
of the pre-CDM model proposed by Richetta and
Odoni (1993).
ATFM in the CDM era also provides fertile ground
for much future research because the scope of potential
OR analysis and modeling has expanded greatly.
Examples of some topics, along with occasional recent
references, include: identifying (as an airline) flights
that should be cancelled or delayed (and by how
much) in connection with GDPs, recovering (as an airline)
from irregular operations (cf. §2) resulting from
GDPs or other ATFM interventions, ensuring equity
of access to airports and ATM resources (Vossen et al.
2003), collaborative routing of aircraft through congested
airspace (Ball et al. 2002), introducing bartering
and possibly market-based mechanisms in the allocation
of airport slots (Vossen and Ball 2001, Hall 1999),
and developing efficient simulation environments for
the testing of alternative ATFM strategies.