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

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.