Transportation Planning and Scheduling for the 2014 Special Olympics USA Games

References

• Baker BM, Ayechew MA (2003) A genetic algorithm for the vehicle routing problem. Comput. Oper. Res. 30(5):787–800.Crossref, Google Scholar
• Beis DA, Loucopoulos P, Pyrgiotis Y, Zografos KG (2006) PLATO helps Athens win gold: Olympic Games knowledge modeling for organizational change and resource management. Interfaces 36(1):26–42.Link, Google Scholar
• Bixby A, Downs B, Self M (2006) A scheduling and capable-to-promise application for Swift & Company. Interfaces 36(1):69–86.Link, Google Scholar
• Bunte S, Kliewer N (2009) An overview on vehicle scheduling models. Public Transport 1(4):299–317.Crossref, Google Scholar
• Carey M, Hendrickson C, Siddharthan K (1981) A method for direct estimation of origin/destination trip matrices. Transportation Sci. 15(1):32–49.Link, Google Scholar
• Cascetta E (1984) Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator. Transportation Res. Part B: Methodological 188(4–5):289–299.Crossref, Google Scholar
• Ceder A (2011) Public-transport vehicle scheduling with multi vehicle type. Transportation Res. Part C: Emerging Tech. 19(3):485–497.Crossref, Google Scholar
• Desaulniers G, Hickman M (2007) Public transit. Barnhart C, Laporte G, eds. Handbooks in Operations Research and Management Science (Elsevier, London), 69–127.Google Scholar
• Ernst AT, Jiang H, Krishnamoorthy M, Sier D (2004) Staff scheduling and rostering: A review of applications, methods and models. Eur. J. Oper. Res. 153(1):3–27.Crossref, Google Scholar
• Frantzeskakis J, Frantzeskakis M (2006) Athens 2004 Olympic Games: Transportation planning simulation and traffic management. Inst. Transportation Engrg. J. 76(10):26–32.Google Scholar
• Giveen SM (1963) A taxicab problem with time-dependent arrival rates. SIAM J. Comput. 5(2):119–127.Google Scholar
• Guihaire V, Hao J-K (2008) Transit network design and scheduling: A global review. Transportation Res. Part A: Policy Practice 42(10):1251–1273.Crossref, Google Scholar
• Lo HP, Zhang N, Lam WHK (1999) Decomposition algorithm for statistical estimation of OD matrix with random link choice proportions from traffic counts. Transportation Res. Part B: Methodological 33(5):369–385.Crossref, Google Scholar
• McFadden D (1974) Measurement of urban travel demand. J. Public Econom. 3(4):303–328.Crossref, Google Scholar
• Meng L, Zhou X (2014) Simultaneous train rerouting and rescheduling on an N-track network: A model reformulation with network-based cumulative flow variables. Transportation Res. Part B: Methodological 67(C):208–234.Crossref, Google Scholar
• Nia SG, Sharif B, Habibzadeh N, Rezvani M (2011) Using the genetic algorithm for optimization of the integrated urban transportation systems. J. Tech. Management Growing Economies 2(1):103–113.Google Scholar
• Pravda.Ru (2008) Beijing Olympics to cost China 44 billion dollars. Accessed June 10, 2015, http://english.pravda.ru/sports/games/06-08-2008/106003-beijing_olympics-0/.Google Scholar
• Special Olympics, Inc. (2015) Accessed June 10, 2015, http://www.specialolympics.org/mission.aspx.Google Scholar
• Special Olympics: Our Athletes (2014) Accessed April 9, 2014, http://www.specialolympics.org/Common/Frequently-Asked-Questions.aspx.Google Scholar
• Varelas T, Archontaki S, Dimotikalis J, Turan O, Lazakis I, Varelas O (2013) Optimizing ship routing to maximize fleet revenue at Danaos. Interfaces 43(1):37–47.Link, Google Scholar
• Wren A, Rousseau J-M (1993) Bus driver scheduling—An overview. Daduna JR, Branco I, Paixão JMP, eds. Computer-Aided Transit Scheduling (Springer-Verlag, Berlin), 173–187.Google Scholar
• Yih-Long C, Sullivan RS (1990) Schedule generation in a dynamic job shop. Internat. J. Production Res. 28(1):65–74.Crossref, Google Scholar
• Yoshitomi Y (2002) A genetic algorithm approach to solving stochastic job-shop scheduling problems. Internat. Trans. Oper. Res. 9(4):479–495.Crossref, Google Scholar