Dynamic resource allocation for intermodal freight transportation with network effects: Approximations and algorithms

Abstract

This paper investigates a dynamic resource allocation problem, in which an intermodal operator attempts to determine the policy that characterizes the optimal quantities of each service product allowed to be sold during each time interval within a finite selling horizon. The problem is formulated as a Markov decision process (MDP) model and a variety of mathematical programming models are developed to approximate the MDP model. A series of policies are obtained from the optimal solutions to the approximation models and theoretical results are provided to characterize the comparisons between the MDP model and the approximation models. Various policies are further evaluated through theoretical analysis and simulation tests. We finally gain insights into the importance of the dynamic decisions, stochastic demands, model re-solving, and integer variables in formulating approximation models.