An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times

Published Online:https://doi.org/10.1287/trsc.36.1.21.570

References

  • Bertsekas D., Tsitsiklis J.Neuro-Dynamic Programming (1996) (Athena Scientific, Belmont, MA) Google Scholar
  • Birge J. Decomposition and partitioning techniques for multistage stochastic linear programs. Oper. Res. (1985) 33(5):989–1007LinkGoogle Scholar
  • Birge J., Louveaux F.Introduction to Stochastic Programming (1997) (Springer-Verlag, New York) Google Scholar
  • Carvalho T. A., Powell W. B. A multiplier adjustment method for dynamic resource allocation problems. Transportation Sci. (2000) 34:150–164LinkGoogle Scholar
  • Chen Z-L., Powell W. A convergent cutting-plane and partial-sampling algorithm for multistage linear programs with recourse. J. Optim. Theory Appl. (1999) 103(3):497–524CrossrefGoogle Scholar
  • Cheung R. K-M., Powell W. B. An algorithm for multistage dynamic networks with random arc capacities, with an application to dynamic fleet management. Oper. Res. (1996) 44(6):951–963LinkGoogle Scholar
  • Cheung R. K-M., Powell W. B. SHAPE: A stochastic hybrid approximation procedure for two-stage stochastic programs. Oper. Res. (2000) 48(1):73–79LinkGoogle Scholar
  • Culioli J-C., Cohen G. Decomposition/coordination algorithms in stochastic optimization. SIAM J. Control Optim. (1990) 28–1403CrossrefGoogle Scholar
  • Dantzig G. Linear programming under uncertainty. Management Sci. (1955) 1:197–206LinkGoogle Scholar
  • Ermoliev Y., Ermoliev Y., Wets R. Stochastic quasigradient methods. Numerical Techniques for Stochastic Optimization (1988) (Springer-Verlag, Berlin, Germany) CrossrefGoogle Scholar
  • Frantzeskakis L., Powell W. B. A successive linear approximation procedure for stochastic dynamic vehicle allocation problems. Transportation Sci. (1990) 24(1):40–57LinkGoogle Scholar
  • Godfrey G. A., Powell W. B. An adaptive, distributionfree approximation for the newsvendor problem with censored demands, with applications to inventory and distribution problems. Management Sci. (2001) 48(8):1101–1112LinkGoogle Scholar
  • Godfrey G. A., Powell W. B. An adaptive, dynamic programming algorithm for dynamic fleet management, II: Multiperiod travel times. Transportation Sci. (2002) 36(1Google Scholar
  • Higle J., Sen S. Stochastic decomposition: An algorithm for two stage linear programs with recourse. Math. Oper. Res. (1991) 16(3):650–669LinkGoogle Scholar
  • Infanger G.Planning Under Uncertainty: Solving Large-scale Stochastic Linear Programs (1994) (The Scientific Press Series, Boyd & Fraser, New York) Google Scholar
  • Jordan W., Turnquist M. A stochastic dynamic network model for railroad car distribution. Transportation Sci. (1983) 17:123–145LinkGoogle Scholar
  • Kall P., Wallace S.Stochastic Programming (1994) (John Wiley and Sons, New York) Google Scholar
  • Pereira M., Pinto L. Multistage stochastic optimization applied to energy planning. Math. Programming (1991) 52:359–375CrossrefGoogle Scholar
  • Powell W. B. A stochastic model of the dynamic vehicle allocation problem. Transportation Sci. (1986) 20:117–129LinkGoogle Scholar
  • Powell W. B. An operational planning model for the dynamic vehicle allocation problem with uncertain demands. Transportation Res. (1987) 21B:217–232CrossrefGoogle Scholar
  • Powell W. B. A review of sensitivity results for linear networks and a new approximation to reduce the effects of degeneracy. Transportation Sci. (1989) 23(4):231–243LinkGoogle Scholar
  • Powell W. B., Carvalho T. A. Dynamic control of logistics queueing network for large-scale fleet management. Transportation Sci. (1998) 32(2):90–109LinkGoogle Scholar
  • Powell W. B., Shapiro J. A., Simão H. P., Fourer R., Coullard C., Owens J. A representational paradigm for dynamic resource transformation problems. Ann. Oper. Res. (2002) (J. C. Baltzer AG). ForthcomingGoogle Scholar
  • Puterman M. L.Markov Decision Processes (1994) (John Wiley and Sons, New York) CrossrefGoogle Scholar
  • Rockafellar R. T.Convex Analysis (1972) 2nd ed.(Princeton University Press, Princeton, NJ) Google Scholar
  • Rockafellar R. T., Wets R. Scenarios and policy aggregation in optimization under uncertainty. Math. Oper. Res. (1991) 16(1):119–147LinkGoogle Scholar
  • Ruszczynski A. Feasible direction methods for stochastic programming problems. Math. Programming (1980) 19:220–229CrossrefGoogle Scholar
  • Ruszczynski A. A linearization method for nonsmooth stochastic programming problems. Math. Oper. Res. (1987) 12(1):32–49LinkGoogle Scholar
  • Sutton R., Barto A.Reinforcement Learning (1998) (The MIT Press, Cambridge, MA) Google Scholar
  • Van Slyke R., Wets R. L-shaped linear programs with applications to optimal control and stochastic programming. SIAM J. Appl. Math. (1969) 17(4):638–663CrossrefGoogle 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.