Multimodal iron ore inbound logistics network design under demand uncertainty
Dezhi Zhang, Nan Ni, Xiaofan Lai, Yajie Liu
Abstract
The paper considers the design of a multimodal and multilayer inbound logistics network for iron ore to be delivered from suppliers to steel plants. To make cost-efficient logistics and transportation planning decisions, steel companies should account for uncertainty in the demand for iron ore and the economies of scale during transshipment process in the port, which are two critical elements affecting decisions. To this end, we propose a two-stage nonlinear stochastic programming model with the first stage determining the choice of the port to perform transshipment operations and the needed capacity, and the second stage selecting transportation modes after demand uncertainty has been realized. To solve this problem, we first reformulate and linearize the model based on a quantity discount policy applied to the transshipment ports, and then, we develop a scenario-based decomposition algorithm. We conduct a case study based on the data of a steel company in China to illustrate the applicability of our proposed model. Moreover, we perform numerical experiments to demonstrate the effectiveness of our algorithm.