Rail networks, supply chains and terminals are full of queues, variability and competing constraints. Stochastix simulates and optimises rail systems, networks, yards and fleets, so goods move faster, at lower cost, with the service levels your customers expect.
Train paths, crossing loops, yards and load/unload cycles interact in ways that cap throughput long before the timetable says they should.
Quay cranes, yards, gates and trucks interact in ways spreadsheets miss. Small variability compounds into big queues.
Where to place DCs, how to flow product, and which lanes and routes to run, a high-stakes design problem with millions of feasible options.
Holding too much ties up cash; too little misses orders. The right policy depends on variability you have to model, not guess.
Simulation, optimisation and forecasting delivered as planning tools and live dashboards your operations team relies on.
Discrete-event twins of rail networks, yards, terminals and end-to-end supply chains to expose bottlenecks and test capacity, layout and timetable changes before you invest.
Vehicle routing, network design and inventory-policy optimisation that cut cost and kilometres while holding service levels.
Schedules for train paths, berths, gates, slots and labour that respect every constraint and adapt as the day changes.
Worked examples of how we approach rail, logistics and ports problems, and the decision each one informs.
A yard-and-quay simulation often shows that yard re-handles and truck turn-time, not crane count, cap throughput, so investment goes to the change that actually lifts berth performance.
A mixed-integer network model re-optimises DC locations and product flow paths against demand and freight rates, stress-tested across growth and disruption scenarios.
A discrete-event model of a heavy-haul rail network, train paths, loading, dumping and maintenance windows, reveals where crossing loops and load cycles, not locomotives, cap tonnes.
Tell us where it hurts and what a good outcome looks like. We'll tell you honestly whether a model will help, and how we'd build it.
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