Chapter 6 • Managerial Support Systems 225A Potpourri of DSS Examples
Virtually every issue of Interfaces, a journal devoted to describing practical applications of management
science and operations research, contains a discussion of one or more new DSSs. To illustrate, we briefly
describe three quite different decision support systems presented in three recent issues of Interfaces.
Continental Airlines employs a DSS to minimize the costs of schedule disruptions caused by
unexpected events such as inclement weather, aircraft mechanical problems, and crew unavailability (Yu,
et al., 2003). Because of such disruptions, crews might not be properly positioned to service their remain-
ing scheduled flights. CALEB Technologies has developed the CrewSolver DSS to generate optimal or
near-optimal crew-recovery solutions to cover open flights and return crews to their original schedules in
a cost-effective manner while honoring government regulations, contractual rules, and quality-of-life
requirements. CrewSolver is a real-time DSS operated by a crew coordinator using a graphical user inter-
face. CrewSolver employs live operations data from the system operation control database as well as a
complete crew file. When a disruptive event occurs, the crew coordinator requests a recovery solution,
and CrewSolver employs a mathematical programming model (solved by a heuristic-based search algo-
rithm) to generate up to three solutions, from which the crew coordinator chooses one. Solutions consist
of reassigning crews from one flight to another, deadheading crews to cover a flight or return back to
base, holding crews at their current location, assigning crews additional duty periods, moving a crew’s
layover to a different city, and using reserve crews to cover flights left uncovered by active crews. The
results from the use of CrewSolver have been impressive. Continental Airlines estimates that it saved
$40 million during 2001 from the use of CrewSolver to recover from four major disruptions: snowstorms
that hit Newark, New Jersey, just before New Year’s Eve and again in March, heavy rains that closed the
Houston airport for a day in June, and the terrorist attacks on September 11, 2001.
A quite different type of DSS has been developed to assist Schlumberger, the leading oilfield
services company, in bidding for and carrying out land seismic surveys (Mullarkey, et al., 2007). One of
the services offered by Schlumberger is seismic surveying, the process of mapping subterranean rock
formations with reflected sound waves, which is an important early step in the identification and
recovery of oil and gas reserves. Carrying out a seismic survey is a complicated logistical operation
lasting up to six months, covering hundreds of square miles, and involving many people. Schlumberger
must bid for seismic survey projects, and thus it must be able to quickly and accurately estimate the cost
of a survey. Mullarkey et al. developed a simulation tool to evaluate the impact of crew sizes, the
amount of equipment employed, the survey area, the survey design, the geographic region, and
weather conditions on survey costs and durations. The simulator involves stochastic elements to
incorporate such factors as equipment failures and the varying speeds of the vehicles and crews used in
the survey. Because the results are stochastic, the simulator is run multiple times for each scenario, that
is, for each set of input factors. The scenarios are varied to arrive at the best cost figures for acceptable
survey durations; Schlumberger can then use the costs in preparing its bid for the project. On four
surveys, the use of the DSS resulted in savings of about $2 million, so the simulator should save
Schlumberger $1.5 to $3 million each year. Although the simulator was constructed for bid estimation,
it has also been used for production planning on existing jobs, and future plans include embedding the
simulator in an “end-to-end decision-support framework for each land seismic job, making the
simulator available for both bidding and executing surveys” (pp. 121–122).
A complex, multipart DSS named RealOpt has been built to enable the public-health infrastructure
in the United States to respond quickly and effectively to public-health emergencies, such as bioterrorist
attacks or pandemics (Lee, et al., 2009). The focus of RealOpt is on mass dispensing of medical
countermeasures for protection of the general public. RealOpt consists of four stand-alone DSSs, which
we will briefly mention. RealOpt-Regional assists in the determination of locations for point-of-dispensing
(POD) facilities within a region, considering the population densities and demographic makeup of the
region. It includes interactive visualization tools to assist users with spatial understanding of the region as
well as a specialized heuristic-based mathematical procedure to arrive at good feasible solutions to the
POD location problem, which is a very complex nonlinear mixed-integer program. RealOpt-POD is a DSS
for facility layout and resource allocation for a POD facility. Using an automatic graph-drawing tool,
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