Modeling and optimization of a road–rail intermodal transport system under uncertain information

Abstract

A realistic road–rail intermodal transport system can be suitably modeled as a hub-and-spoke (H&S) network for which the parameters are subject to fuzzy uncertainty: demand, cost and time. For modeling uncertainty, we present a bi-objective optimization formulation for the hub-and-spoke based road–rail intermodal transportation (HS-RRIT) network design problem by taking into account the expected value criterion and the critical value criterion. Using the weighted sum method, we reformulate a single-objective mixed-integer linear programming (MILP) model to solve the equivalent HS-RRIT network design problem. Given the inherent complexity for solving this problem, we develop a memetic algorithm (MA) to obtain high quality solutions. This algorithm utilizes a genetic search method to explore the search space and two different local search strategies called shift and exchange to exploit information in the search region. Finally, we conduct computational analysis over the Turkish network data set to demonstrate the applicability of proposed model and the effectiveness of solution method.