A two-stage stochastic model for intermodal terminal location and freight distribution under facility disruptions

Abstract

A two-stage stochastic model is developed for intermodal facility location and freight distribution under random disruptions at shipper facilities and/or intermodal terminals (IMTs). The magnitude of the disruption and the impacted locations are uncertain parameters. A two-stage stochastic programming model is used to address supply uncertainty at shippers and throughput capacity uncertainty at IMTs. A level-method based decomposition approach and the L-shaped method are used to solve the model. The state of South Carolina in the U.S.A. is used as a case study with the goal of determining the set of IMT locations that minimise the total long-run network costs due to hurricane disruptions. A methodology is developed to generate realistic scenarios. The Freight Analysis Framework Version 4.5 data set is used to generate demands and supply, and k-means clustering is used with the Hurricane database (HURDAT2) to generate hurricane disruption scenarios. Sensitivity analyses are performed by varying the disruption probabilities, disruption duration, and direct shipping cost parameters. The results indicate that as disruptions increase, less disrupted intermodal facilities are opened. Also, as direct shipping costs increase, the long-term savings increase non-linearly for all magnitudes of disruptions.