Meta-Heuristics for a Class of Demand-Responsive Transit Systems

Published Online:https://doi.org/10.1287/ijoc.1030.0051

References

  • Boyan J. A., Moore A. W. Learning evaluation functions for global optimization and Boolean satisfiability. Proc. 15th National Conf. Artificial Intelligence 10th Conf. Innovative Appl. Artificial Intelligence (1998) (AAAI Press, Menlo Park, CA) 3–10Google Scholar
  • Crainic T. G., Malucelli F., Nonato M. A demand responsive feeder bus system. CD-ROM 7th World Congress Intelligent Transport Systems (2000) Torino, Italy7WC-ITSGoogle Scholar
  • Crainic T. G., Malucelli F., Nonato M. Flexible many-to-few + few-to-many = an almost personalized transit system. Preprints TRISTAN IV—Triennial Sympos. Transportation Analysis (2001a) 2(Faculdade de Ciências da Universidade de Lisboa and Universidade dos Açores, São Miguel, Açores, Portugal) 435–440Google Scholar
  • Crainic T. G., Malucelli F., Nonato M. Meta-heuristics for a class of demand-responsive transit systems. (2001b) . Publication CRT-2001-28, Centre de recherche sur les transports, Université de Montréal, Montréal, QC, CanadaGoogle Scholar
  • Feo T. A., Resende M. G. C. Greedy randomized adaptive search procedures. J. Global Optim. (1995) 6:109–133CrossrefGoogle Scholar
  • Fiorenzo Catalano M. S., Malucelli F. Randomized heuristic schemes for the set covering problem. Internat. J. Comput. Res. (2001) 10:531–546Google Scholar
  • Gendreau M., Laporte G., Potvin J.-Y., Toth P., Vigo D. Metaheuristics for the vehicle routing problem. The Vehicle Routing Problem (2002) 9(SIAM, Philadelphia, PA) 129–154SIAM Monographs on Discrete Mathematics and ApplicationsCrossrefGoogle Scholar
  • Glover F., Laguna M.Tabu Search (1997) (Kluwer Academic Publishers, Norwell, MA) CrossrefGoogle Scholar
  • Guignard M., Kim S. Lagrangean decomposition: A model yielding stronger lagrangean bounds. Math. Programming (1987) 39:215–228CrossrefGoogle Scholar
  • Hickman M., Blume K., Voss S., Daduna J. R. Modeling cost and passenger level of service for integrated transit service. Computer-Aided Scheduling of Public Transport (2001) 505(Springer, Berlin, Germany) 233–251Lecture Notes in Economics and Mathematical SystemsCrossrefGoogle Scholar
  • Horn M. E. T. Fleet scheduling and dispatching for demand-responsive passenger service. Transportation Res. Part C (2002) 10:35–63CrossrefGoogle Scholar
  • Ioachim I., Desrosiers J., Dumas Y., Solomon M. M., Villeneuve D. A request clustering for door to door handicapped transportation. Transportation Sci. (1995) 29:63–78LinkGoogle Scholar
  • Laporte G. The traveling salesman problem: An overview of exact and approximate algorithms. Eur. J. Oper. Res. (1992a) 59:231–247CrossrefGoogle Scholar
  • Laporte G. The vehicle routing problem: An overview of exact and approximate algorithms. Eur. J. Oper. Res. (1992b) 59:345–358CrossrefGoogle Scholar
  • Lawler E. L., Lenstra J. K., Rinnoy Kan A. H. G., Shmoys D. B.The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization (1985) (John Wiley, New York) Google Scholar
  • Liaw C. F., White C. C., Bander J. A decision support system for the bimodal dial-a-ride problem. IEEE Trans. Systems, Man Cybernetics, Part A (1996) 25:552–565CrossrefGoogle Scholar
  • Malucelli F., Nonato M. Formulations and bounding procedures for optimization problems arising in managing demand adaptive transit systems. (2003) . Working paper, Politecnico di Milano, Milan, ItalyGoogle Scholar
  • Malucelli F., Nonato M., Pallottino S., Ciriani T. A., Gliozzi S., Johnson E. L., Tadei R. Some proposals on flexible transit. Oper. Res. Indust. (1999) (Purdue University Press, Ashland, OH) 157–182CrossrefGoogle Scholar
  • Malucelli F., Nonato M., Crainic T. G., Guertin F., Voss S., Daduna J. R. Adaptive memory programming for a class of demand-responsive transit systems. Computer-Aided Scheduling of Public Transport (2001) 505(Springer, Berlin, Germany) 253–273Lecture Notes in Econom. Math. SystemsCrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.