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Home›Business Developments & Projects›MOL launches AI-powered, vessel allocation support system
Business Developments & Projects

May 28, 2021 · about 5 years ago

MOL launches AI-powered, vessel allocation support system

Japanese shipping major Mitsui O.S.K. Lines (MOL) and its group company MOL Information Systems have started the operation of a support system for car carrier allocation planning based on mathematical optimization. The introduction of this type of system is expected to enable speedy decision-making

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MOL

Japanese shipping major Mitsui O.S.K. Lines (MOL) and its group company MOL Information Systems have started the operation of a support system for car carrier allocation planning based on mathematical optimization.

The introduction of this type of system is expected to enable speedy decision-making and greater flexibility in meeting changes in transport demand.

In addition, improvement of fleet-wide efficiency is said to lower fuel consumption per unit transported, also reducing the environmental impact of fleet operation.

In September 2019, MOL developed the algorithm system in cooperation with professor Shunji Umetani Osaka University.

As informed, the algorithm elicits the optimal allocation plan from an enormous number of combinations using mathematical optimization or mathematical programming, which can enable companies to solve complex business problems and make better use of available resources and data.

In order to achieve optimal fleet deployment companies need to plan transport routes connecting automobile manufacturing plants and markets around the globe, and it is critical to keep the entire fleet operating at peak efficiency.

That means the company needs to simulate transport routes per vessel several months ahead of time, and to achieve optimal, fleetwide solutions from theoretically millions of possible combinations involving all of its vessels, as revealed in the statement.

With the car carrier allocation plan, MOL also started the development of a cargo loading plan support system based on the same optimization method.

The aforementioned system will serve, for example, when the vessel is calling several loading and unloading ports.

Since the order of cargo loading/unloading and hull balance during the voyage must be taken into consideration, it can take longer to develop a loading plan, depending on the plan’s level of difficulty and the skills of the planner.

The system is expected to significantly shorten the required time to respond to customers when transport volume or the order of port calls changes suddenly.

The AI-powered support systems are a part of MOL’s digitalization transformation expected to improve transport service quality.

MOL currently operates about 100 car carriers, and in general, cargo capacity per ship is around 5,000 standard passenger cars.

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