Seminar: Train timetabling and destination selection in mining freight rail networks: A hybrid simulation methodology, 26th May, 1PM

When: Thursday 26th of May, 1PM AEDT

Where: The talk will be presented online via Zoom, RSVP here.

Speaker: William Jones

Title: Train timetabling and destination selection in mining freight rail networks: A hybrid simulation methodology

Abstract:

This talk presents a hybrid simulation methodology designed to support freight rail operations in the mining industry.  The methodology can facilitate operations planning to determine train destinations across a network and generate a feasible timetable that satisfies operational needs. The method combines discrete-event simulation and agent-based modelling with heuristics to govern train movements destination selection, incorporating an ensemble of simulation runs. The capability of our method to produce a train timetable that satisfies the requirements of the mining operation is demonstrated. Choosing optimal destinations from many options for a large fleet of trains in a vast network is a significant computational challenge (NP-hard in the general case). The method presented significantly reduces the parameter space for which full enumeration of all options would not be computationally tractable. The presentation explains why at a conceptual level the hybrid model design and adopted modelling frame are well suited to the problem at hand.

Bio:

Dr William Jones is a Research Associate at the Rio Tinto Centre for Mine Automation, Australian Centre for Field Robotics, University of Sydney, AU. He earned a B.Sc. Eng. in Mechanical Engineering from Cardiff University (2013) and an EngD in Systems Engineering (2019) from The University of Bristol, UK. His research interests include simulation and optimisation of multi-agent systems, automation and the process of model development. His current work focuses on developing a digital twin of Rio Tinto’s rail network incorporating novel scheduling and planning algorithms to optimise train movements, taking advantage of the autonomous trains’ performance and reliability. Previously he worked with Eurostar International Limited (the high-speed rail operator linking London and continental Europe via the channel tunnel) in partnership with Kent Business School to develop digital twins of their stations and rail operation. These were used to increase passenger flow through stations and improve the robustness of operations. The models developed were key in preparing for disruption due to BREXIT.

Contacts

Sydney Institute for Robotics and Intelligent Systems
info@acfr.usyd.edu.au