Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue
ABSTRACT
The Saudi Public Transport Company (SAPTCO) intercity bus schedule comprises a list of 382 major trips per day to over 250 cities and villages with 338 buses. SAPTCO operates Mercedes 404 SHD and Mercedes 404 RI-IL fleet types for the intercity trip. The fleet assignment model developed by American Airlines was adapted and applied to a sample of the intercity bus schedule. The results showed a substantial saving of 29% in the total number of needed buses. This encourages the decision makers at SAPTCO to use only Mercedes 404 SHD fleet type. Hence, the fleet assignment model was modified to incorporate only one fleet type and applied to the sample example. Due to the increase in the problem size, the model was decomposed by stations. Finally, the modified decomposed model was applied to the whole schedule. The model results showed a saving of 16.5% in the total number of needed buses of Mercedes 404 SHD. A sensitivity analysis was carried out and showed that the predefined minimum connection time is critical for model efficiency. A modification to the connection time for 11 stations showed a saving of 14 more buses. Considering our recommendation of performing a field study of the trip connection time for every station, the expected saving of the total number of needed buses will be about 27.4% (90 buses). This will yield a net saving of 16.44 million Saudi Riyals (USD 4.4 million) per year for SAPTCO in addition to hiring new employees. The revenue analysis shows that these 90 surplus buses will yield about USD 20,744,000 additional revenue yearly.
The Saudi Public Transport Company (SAPTCO) intercity bus schedule comprises a list of 382 major trips per day to over 250 cities and villages with 338 buses. SAPTCO operates Mercedes 404 SHD and Mercedes 404 RI-IL fleet types for the intercity trip. The fleet assignment model developed by American Airlines was adapted and applied to a sample of the intercity bus schedule. The results showed a substantial saving of 29% in the total number of needed buses. This encourages the decision makers at SAPTCO to use only Mercedes 404 SHD fleet type. Hence, the fleet assignment model was modified to incorporate only one fleet type and applied to the sample example. Due to the increase in the problem size, the model was decomposed by stations. Finally, the modified decomposed model was applied to the whole schedule. The model results showed a saving of 16.5% in the total number of needed buses of Mercedes 404 SHD. A sensitivity analysis was carried out and showed that the predefined minimum connection time is critical for model efficiency. A modification to the connection time for 11 stations showed a saving of 14 more buses. Considering our recommendation of performing a field study of the trip connection time for every station, the expected saving of the total number of needed buses will be about 27.4% (90 buses). This will yield a net saving of 16.44 million Saudi Riyals (USD 4.4 million) per year for SAPTCO in addition to hiring new employees. The revenue analysis shows that these 90 surplus buses will yield about USD 20,744,000 additional revenue yearly.
KEYWORDS
Fleet Assignment Model, Bus Scheduling, Integer Programming, Transportation Service, Revenue Managemen
Fleet Assignment Model, Bus Scheduling, Integer Programming, Transportation Service, Revenue Managemen
Cite this paper
nullM. Hasan and A. Hammad, "Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue," Journal of Service Science and Management, Vol. 3 No. 3, 2010, pp. 373-382. doi: 10.4236/jssm.2010.33044.
nullM. Hasan and A. Hammad, "Intercity Bus Scheduling for the Saudi Public Transport Company to Maximize Profit and Yield Additional Revenue," Journal of Service Science and Management, Vol. 3 No. 3, 2010, pp. 373-382. doi: 10.4236/jssm.2010.33044.
References
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[1] C. Barnhart, A. Farahat and M. Lohatepanont, “Airline Fleet Assignment with Enhanced Revenue Modeling,” Operations Research, Vol. 57, No. 1, 2009, pp. 231-244. [2] J. Abara, “Applying Integer Linear Programming to the Fleet Assignment Problem,” Interfaces, Vol. 19, No. 4, 1989, pp. 20-28. [3] R. Subramanian, R. Scheff, J. Quillinan, S. Wiper and R. Marsten, “Coldstar: Fleet Assignment at Delta Air Lines,” Interfaces, Vol. 24, No. 1, 1994, pp. 104-120. [4] S. Kontogiorgis and S. Acharya, “US Airways Automates Its Weekend Fleet Assignment,” Interfaces, Vol. 29, No. 3, 1999, pp. 52-62. [5] B. Rexing, C. Barnhart, T. Kniker, A. Jarrah and N. Krish- namurthy, “Airline Fleet Assignment with Time Windows,” Transportation Science, Vol. 34, No. 1, 2000, pp. 1-20. [6] R. Ahuja, J. Goodstein, A. Mukherjee, J. Orlin and D. Sharma, “A Very Large-Scale Neighborhood Search Algorithm for the Combined Through-Fleet-Assignment Mo- del,” INFORMS Journal on Computing, Vol. 19, No. 3, 2007, pp. 416-428. [7] K. T. Talluri, “Swapping Applications in a Daily Fleet Assignment,” Transportation Science, Vol. 30, No. 3, 1996, pp. 237-248. [8] H. Sherali and H. Zhu, “Two-Stage Fleet Assignment Mo- del Considering Stochastic Passenger Demands,” Operations Research, Vol. 56, No. 2, 2008, pp. 383-399. [9] T. Jacobs, B. Smith and E. Johnson, “Incorporating Network Flow Effects into the Airline Fleet Assignment Process,” Transportation Science, Vol. 42, No. 4, 2008, pp. 514-529.