Throughput optimisation in a coal export system with multiple terminals and shared resources. (August 2019)

• Record Type: Journal Article
• Authors: Rocha de Paula, Mateus; Boland, Natashia; Ernst, Andreas T.; Mendes, Alexandre; Savelsbergh, Martin
• Is Part Of: Computers & industrial engineering. Volume 134(2019)
• Page Start: 37
• Page End: 51
• Publication Date: 2019-08
• Subjects: Coal supply chain -- Optimization -- Genetic algorithms -- Logistics -- Mining industry; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.05.021
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Method was applied to the largest coal export system in the world by volume. The new approach generates a 17% reduction in average vessel delay. Computational times are considerably lower than the previous state-of-the-art method. Very detailed modelling of terminal activities and resource allocation. Abstract: This work describes a genetic algorithm based approach for the optimization of the Hunter Valley coal export system in Newcastle, Australia. The Port of Newcastle features three coal export terminals, operating primarily in cargo assembly mode. They share a rail network on their inbound operations and a channel on their outbound operations. Maximizing throughput at a single coal terminal, taking into account its layout, equipment and operating policies, is already a challenging problem. However, maximizing throughput of the Hunter Valley coal export system as a whole requires that terminals and inbound/outbound shared resources be considered simultaneously. Existing approaches to solve this and similar problems either lack realism or are computationally too demanding to be useful as an everyday planning tool. We present a parallel genetic algorithm to optimize the integrated system. The algorithm models activities in continuous time and can handle practical planning horizons efficiently. The solutions are on average 17% better than those obtained with the current state-of-the-art method – a constraint programming-based approach – requiring less than 3% of the CPU time. Tests were conducted on 10 instances generated using real world data, with 200 vessels and approximately 270 stockpiles each.