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

ABSTRACT

We present a hybrid simulation methodology designed to support freight rail operations in the mining industry. We aim to bridge the gap between hybrid simulation modelling research and practice. Through discussion of a case study, we contribute to the hybrid simulation methodological literature, explaining why at a conceptual level the hybrid model design and adopted modelling frame are well suited to the problem at hand. The methodology we present can be used in mining freight rail 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. We demonstrate the capability of our method to produce a train timetable that satisfies the requirements of the mining operation. 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.

KEYWORDS:

• Hybrid simulation
• freight rail
• scheduling

Acknowledgment

This work was supported by the Rio Tinto Centre for Mine Automation and the Australian Centre for Field Robotics, University of Sydney, Australia.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

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