@article{Fan:208244,
      recid = {208244},
      author = {Fan, Wei and Machemehl, Randy},
      title = {A Multi-stage Monte Carlo Sampling Based Stochastic  Programming Model for the Dynamic Vehicle Allocation  Problem},
      address = {2004-03},
      number = {1425-2016-118390},
      pages = {26},
      year = {2004},
      abstract = {Optimization under uncertainty has seen many applications  in the industrial world. The objective of this paper is to  study the stochastic dynamic vehicle allocation problem  (SDVAP), which is faced by many trucking companies,  container companies, rental car agencies and railroads. To  maximize profits and to manage fleets of vehicles in both  time and space, this paper has formulated a multistage  stochastic programming based model for SDVAP. A Monte Carlo  Sampling Based Algorithm has been proposed to solve SDVAP.  A probabilistic statement regarding the quality of the  solution from the Monte Carlo sampling method is also  identified by introducing a lower bound and an upper bound  of the obtained optimal solution. A five-stage experimental  network was introduced for demonstration of this algorithm.  The computational results indicated a solution of high  quality when Monte Carlo sampling based algorithm is used  for solving SDVAP, strongly suggesting that these  algorithms can be used for real world applications for  decision making under uncertainty.},
      url = {http://ageconsearch.umn.edu/record/208244},
      doi = {https://doi.org/10.22004/ag.econ.208244},
}