A computational comparison of cargo prioritization and terminal allocation problem models. (June 2020)

• Record Type: Journal Article
• Authors: Delgado-Hidalgo, Liliana; Rainwater, Chase; Nachtmann, Heather
• Is Part Of: Computers & industrial engineering. Volume 144(2020)
• Publication Date: 2020-06
• Subjects: Disruption response -- Freight transportation -- Cargo prioritization -- Inland waterways -- Integer programming -- Valid Inequalities; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2020.106450
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We study how to redirect and prioritize barge offloading during disruption event. We formulate a mixed integer linear programming model to offload disrupted barges. The model minimizes the cargo value loss during inland waterway disruptions. Our method outperforms a non-linear model solved with a genetic algorithm approach. Our method outperforms a non-linear model solved with a tabu search approach. Abstract: Inland waterway disruptions may interrupt barge navigation, resulting in significant economic and environmental consequences. Disruption response reroutes disrupted barges to accessible terminals to offload cargo water to land transportation. We investigate how to redirect disrupted barges and prioritize offloading at terminals to minimize the total cargo value loss during inland waterway disruption response. This problem is known in the literature as the cargo prioritization and terminal allocation problem (CPTAP). Previous studies formulated the CPTAP as a non-linear integer programming (NLIP) model, which was solved with a genetic algorithm (NLIPGA) and a tabu search (NLIPTS) approach. In this article, we formulate CPTAP as a mixed integer linear programming (MILP) model and improve its performance through the addition of valid inequalities, which we refer to as MILP'. Due to problem complexity, the NLIPGA and NLIPTS results were validated for small size instances. We fill this gap by using the lower bounds of MILP' model to validate the quality of NLIPGA and NLIPTS solutions, and we compare the MILP' with the NLIPGA and the NLIPTS solutions for multiple scenarios. The MILP' formulation is found to outperform the NLIPGA and NLIPTS approaches by reducing the total cargo value loss.