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Sensitivity Analysis of a Dynamic Fleet Management Model Using Approximate Dynamic Programming

School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York 14853
Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544
topaloglu{at}orie.cornell.edu
powell{at}princeton.edu

We present tractable algorithms to assess the sensitivity of a stochastic dynamic fleet management model to fleet size and load availability. In particular, we show how to compute the change in the objective function value in response to an additional vehicle or an additional load introduced into the system. The novel aspect of our approach is that it does not require multiple simulations with different values of the model parameters, and in this respect it differs from trial-and-error-based "what-if" analyses. Numerical experiments show that the proposed methods are accurate and computationally attractive.

Subject classifications: transportation; models; network; dynamic programming/optimal control; applications.
History: Received February 2004; revision received February 2006; accepted February 2006.