The scale of the tanker problem
Crude oil still spurs the growth of nations and commerce, and will for some years to come. For oil majors the primary challenge is supply and demand — getting the right grade to the right port at the right time, while minimising cost. Approximately 60% of the world's oil is transported by tankers; over 3,500 are in service worldwide.
Tanker class is dictated by the area or canal a vessel needs to traverse: Panamax (230m / max DWT 80,000), Aframax (245m / 120,000), Suezmax (285m / 200,000), VLCC (330m / 320,000) and ULCC (415m / 550,000). Each class has its own port-access and route-feasibility profile. None of that is optional to model.
Ten challenges that make manual scheduling unforgiving
Tanker operation teams have to schedule across multiple vessels, multiple oil grades, multiple load and discharge ports, while keeping tanks at every discharge port above their minimum safety stock. The following ten challenges are not hypothetical — they are the standing list every planner works against.
“Many organisations perform this manually. The cost of operational inefficiency is seldom or never computed.”
- Manual scheduling is expensive — a 90-day schedule across multiple vessels, multi-grade cargo, multiple load and discharge ports is time-consuming to assemble by hand
- Meeting demand at discharge ports — stocks must never dip below minimum safety levels
- Dealing with uncertainties — congestion, vessel breakdown, berth maintenance, planned off-hires, arrival at specific port windows
- Demand volatility — daily consumption per grade per discharge port shifts with holidays, season and weather
- Pressure to maximise time-charter utilisation, while respecting draft, air draft, LOA, beam, vessel age, vessel flag and dry-docking constraints
- Incompatible oil-grade loading — each loaded grade has to be compatible with the previous voyage, or tank cleaning time has to be scheduled in
- Finding spot-charter vessels in the market when the available fleet is fully deployed
- Port constraints — single-berth availability, daytime-only arrivals, draft limitations, tidal ranges, waiting time, flag age restrictions, port maintenance
- Schedule disruptions — adverse weather, strikes, blocked canals, accidents, country unrest
- Management pressure to maximise vessel and berth utilisation at the lowest possible cost
How the optimiser builds a 90-day schedule in minutes
The Tanker Fleet and Service Optimizer combines an optimisation engine with machine learning. ML generates the demand forecast at each discharge port; the optimiser produces the schedule against that forecast while respecting every constraint. The primary objectives are explicit: minimise cost, satisfy demand at the discharge port, maximise time-charter utilisation.
The output spans 30, 60 or 90 days and drills down to weeks or days per vessel. The schedule covers multiple vessels delivering multi-grade oil cargo from multiple load ports to multiple discharge ports.
The twelve things the optimiser handles
The features below are the ones that distinguish a tanker schedule that holds together from one that does not. Each is a discrete capability of the optimiser, not an aspiration.
- 30 / 60 / 90-day schedules generated in minutes
- Multiple oil grades across multiple vessels, load ports and discharge ports
- Stock management — discharge-port consumption met without dipping below safety stock
- Reliability — schedule survives congestion, vessel breakdown, berth maintenance, off-hires and port-window timing
- Data-driven forecast — daily forecast per grade per discharge port using AI and ML
- Time-charter optimisation — ideal number of vessels of mixed sizes for maximum utilisation, with draft/air-draft/LOA/beam/age/flag/dry-dock compliance
- Spot charter — highlights when deployed time-chartered tonnage cannot meet demand and a spot vessel must be added
- Grade-load optimisation — loaded grade compatible with previous voyage, with tank-cleaning periods inserted when not
- Port constraints — daytime arrival, draft, tidal range, minimum waiting time, all factored automatically
- Agile planning — schedule re-runs to satisfy demand after disruption
- Reduced costs — vessel-vs-vessel cost comparisons and revenue per ton-mile
- Disruption planning — replanning after disruptions reported by vessel or at port, which is where most of the cost saving accrues
Where the value actually lands
The headline objective is minimised cost of operations. Underneath that, the optimiser ensures demand is satisfied and no port runs dry, maximises vessel utilisation, minimises waiting time at port due to tide / congestion / maintenance / dry-docking, allows replanning after any disruption at the discharge port or in transit, facilitates the selection and allocation of time-chartered and spot-chartered vessels at the load region, and ensures every technical port and vessel constraint is met.
None of these are theoretical claims. They are the gap between a manual schedule and one produced by an engine that holds every constraint in memory at once.