By Michael F. Argüello, Jonathan F. Bard (auth.), Gang Yu (eds.)
260 2 group Legalities and workforce Pairing fix 264 three version and Mathematical formula 266 four resolution method 271 five Computational stories 277 6 end 285 REFERENCES 286 10 using OPTIMIZATION to accomplish AIR site visitors circulation administration Kenneth Lindsay, E. Andrew Boyd, George sales space, and Charles Harvey 287 1 creation 288 2 The site visitors circulation administration (TFM) challenge 289 three contemporary TFM Optimization versions 292 four The Time task version (TAM) 302 five precis and Conclusions 307 REFERENCES 309 eleven THE methods OF AIRLINE approach OPERATIONS regulate Seth C. Grandeau, Michael D. Clarke, and Dennis F.X. Mathaisel 312 1 creation 313 2 The 4 levels of Airline agenda improvement 315 The Airline Operations keep watch over middle (OCC) three 320 four research of Operational difficulties 331 five components For development 352 6 Case examine: PT Garuda Indonesia airways 357 REFERENCES 368 12 THE complicated CONFIGURATION version Bruce W. Patty and Jim Diamond 370 1 advent 370 challenge Description 2 371 challenge formula three 375 four version Implementation 379 ix Contents 383 five precis REFERENCES 383 thirteen built-in AIRLINE agenda making plans Cynthia Barnhart, Fang Lu, and Rajesh Shenoi 384 1 advent 385 2 Fleet project and group Pairing difficulties: present M- els and Algorithms 388 three An built-in Approximate Fleet project and team Pa- ing version 393 four a complicated built-in resolution process 395 five Case examine 396 6 Conclusions and destiny examine instructions 399 REFERENCES 401 14 AIRLINE agenda PERTURBATION challenge: touchdown AND TAKEOFF WITH
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Extra info for Operations Research in the Airline Industry
Example text
This implies that a uniform cost of $20 per minute of delay is assessed. Figure 5 illustrates the time-band approximation network constructed for this sample problem. The time-band lengths are thirty minutes. Each station-time node represents thirty minutes from the beginning of the associated time. m. m. In this network, nodes 5, 11, 19, and 24 are station-sink nodes while every other node is a station-time node. The same network is used for each scenario. The only distinction between scenarios is the placement of aircraft supply and demand at the station-time and station-sink nodes.
Assume that the minimum turnaround time is one- half hour and the station departure curfew is midnight. The time segment is defined as one hour. Thus each of the station-time nodes labeled 1, 2, and 3, represents an hour of time at the station. Node 4 is the station-sink node. m. m. m. The arc from node 1 to node 4 allows the aircraft made available in the first hour to terminate at the station. Nodes 2 and 3 have two and one inbound flight arcs, respectively. m. m. m. and midnight. m. m. This is because outbound flight arcs originate from node 3 and a 30 minute turnaround time is assumed.
A side constraint requiring flow on each flight arc must be added. This assures that flights are either assigned or canceled. To guarantee that solutions are complete, flow along each flight arc must be either 0 or 1. If the cancellation source nodes, and hence the cancellation flow, were to be removed from the network, the minimum cost flow problem with the flight cover side constraint that remains becomes a minimum cost single commodity binary flow with homologous arcs problem. The homologous arcs are the set of flight arcs.