The Client operates a heavily constrained and complex rail logistics operation that moves grains of various types from over 50 inland locations and 3 main ports (see Appendix A).

The Client wanted to know how much additional rollingstock (locomotives and wagons) it would need to purchase to increase their monthly peak railed tonnes by 50% over a 10-year period.

Considerations

• Usage of track is shared by other private and government railways
• Different speed restrictions are applied to the track during different times of the year
• There are two different gauges or rail to and several different configurations of locomotive-wagon pairing. The model needed to recommend new quantities of the appropriate class and size.
• Each depot or site has a unique loading delay and constraint for how loading work

The Solution & Method

The Greennet team worked with the client to first establish the current baseline of operations and system throughput. This model became the ‘digital twin’ of the network representing the different consists, locations and travel times accurately to with 5 % of a ‘peak’ production month.

Once the model was validated, additional fleet was added into the system incrementally to find the maximize throughput the rail network could deliver with the least amount of assets.

Key metrics tracked in the simulation model were as follows:

• # Tonnes delivered to each port per day
• # Tonnes loaded from each grain depot/site each day
• Queuing at each Port location
• Port and Depot stock levels
• Port and Depot load / unload times
• Route and section travel times

The Results & Conclusions

• The Port infrastructure was going to be the primary constraint in the future for capacity regardless of how many rolling stock assets were on the network (see Appendix B).
• With the Port infrastructure upgrades added into the model, the minimum number of rolling stock assets was able to be simulated to archive the throughput
• Loading efficiency was a highly sensitive factor in terms of hitting targets

The simulation work performed on this project was critical to understanding the right amount of capital spend on procurement of assets as well as informing other changes that would need to take place at a system level to facilitate the desired throughput increases.

The table below was able to be supplied to the Client as a final deliverable.

Appendix A

Appendix B