The decision problem at the centre of liner operations
Liner operations teams face a tremendous task: scheduling vessels optimally on manual or outdated systems while keeping costs down. The variables in play are unforgiving — expanding trade wars, general economic instability, the ratio between chartered and owned vessels, bunker volatility (typically 50–60% of total operating costs), port congestion, blanked sailings, daylight-arrival-only ports, weather and political disruptions, and freight rates that rise and dip with the market.
The hardest part is not picking a vessel for a service — it is creating a vessel schedule that matches cargo demand while maximising contribution. Speed, price and timing all pull in different directions. Liner companies are confronted by tough decisions about which factor takes precedence on any given week.
“The true hidden cost behind manual scheduling is the cost of operational inefficiency. This is seldom or never computed or even discussed.”
The limiting factors a scheduler must respect
Before the optimiser runs, the constraints have to be specified. They fall into three broad groups: vessel-side, port-side, and equipment-side. None of them can be ignored without producing a schedule that fails in execution.
- Vessel size, cargo demand, tides, draft limitations, berth window constraints
- MT container supply, route constraints, operational constraints, commercial constraints
- Vessel-side: max and economical speed, fuel consumption at multiple speeds, bunker cost, charter hire, port and canal costs, insurance and maintenance
- Port-side: load and discharge restrictions, allowed draft, LOA, beam, crane availability, air draft, terminal productivity, nighttime arrival restrictions, high-tide arrival, port costs, terminal handling charges by equipment, seasonal weather variation, port congestion
- Empty-equipment-side: imbalance by port, stock status, minimum stock threshold, repositioning costs, one-way lease moves, port-pair TEU and weight constraints, service contribution per TEU, minimum TEU commitment per customer
The hybridised approach — optimiser plus AI
OptiFleet uses an optimisation engine combined with machine learning. The optimiser handles the constraint-satisfaction problem — fitting vessels to services within hard limits — while machine learning generates the demand and supply context the optimiser runs against.
From there it gets to work on the actual scheduling work: port rotation, distance between port legs, vessel speed per leg, terminal productivity, port stay, total sea time, manoeuvring time, and service frequency. Each vessel's economics are factored in alongside: maximum and economical speed, fuel consumption at different speeds, bunker cost, charter hire, port and canal costs, insurance and maintenance.
What the optimiser actually decides
When OptiFleet runs, it produces a set of specific decisions for the planner — not just a forecast. Each of these is a discrete output the operations team can execute against.
- The port pairs — sets of load ports and discharge ports — for each service
- Quantity of TEU and weight for each port pair
- Which services to run
- List of vessels per service, with load and discharge dates
- Preferred customer and commodity assignment by equipment type
- Quantity of empty repositioning between surplus and deficit locations
- Maximised vessel utilisation across the network
What the planner gets out
OptiFleet automates and delivers insights graphically — financial profitability, the commercial plan, the operational plan and the empty-equipment plan, all rendered in one view. The optimiser can then be instructed to produce a fleet and service schedule that maximises profitability per vessel or per service.
Beyond the initial schedule, the same application analyses demand forecasts, optimises routes for fuel efficiency, monitors capacity by vessel size or engine class, forecasts market supply and demand, and factors in weather data — with a few clicks — so the schedule keeps producing better decisions over time rather than freezing once it ships.