Nippon Yusen Kaisha (hereinafter “NYK”), NYK Group company MTI Co. Ltd. (hereinafter “MTI”), and GRID Inc. (hereinafter “GRID”) have started developing a model using AI to optimize the efficiency of ship allocation plans* of pure car and truck carriers (PCTCs). NYK operates about 120 PCTCs, the largest fleet among shipping companies worldwide.
The ship allocation plan, which determines which port a vessel will sail to start the next voyage once the current voyage ends, is generally formulated by a skilled person after consideration of various conditions such as cargo demands, ship schedule, vessel type, and ship loading capacity.
NYK currently utilizes its own in-house ship allocation planning system but has faced difficulties dealing with various decision-making factors and situations that can change from moment to moment. In addition, as efforts for decarbonization accelerate within the shipping industry, operating these 120 PCTCs as efficiently as possible has become an imperative issue to reduce greenhouse gases (GHGs) emitted from ships.
Development of optimization system
To address these topics, NYK, MTI, and GRID are now collaborating to optimize the ship allocation plans for NYK’s PCTC fleet. These three parties aim to build an optimization model for ship allocation plans by combining NYK’s know-how in formulating ship allocation plans, MTI’s simulation technology in ship operations, and the AI technology of GRID, a technology venture specializing in social infrastructure.
In this collaboration, digital twins** and the latest machine learning technologies will be introduced, in addition to the mathematical optimization technology that has been used in NYK’s own in-house system. Not only building of the optimization model but also application development will be included in the scope of this collaboration.
NYK, MTI, and GRID are aiming to reduce GHGs from ships, as well as improve the efficiency of the planning process, and the three companies will continue to optimize future models and systems. Trials of this system are planned for June 2022, with full operation targeted for 2024.
For NYK and MTI, this collaboration is part of the “NYK Group ESG Story,”*** in addition to the Sail GREEN Project**** being promoted by NYK’s Automotive Transportation Headquarters. For GRID, this collaboration is a powerful, concrete example of “GX that achieves both decarbonization and economic benefits”***** through GRID’s ReNom apps, an AI development platform to solve business problems through machine learning / deep learning. NYK, MTI, and GRID will promote the conversion of daily work to AI and contribute to the realization of a decarbonized society and work-style reform through DX in the shipping industry.
* A ship allocation plan determines what voyage a ship will be assigned to, and the “sailing plan” determines the schedule of the ship. The “loading plan” determines how the vehicles are loaded inside the ship.
** Digital twin is a technology that reproduces a real space or objects in a digital, virtual space. Information such as the working situation of the object and conditions of each part of the object are reflected within a digital simulator. This technology can also be adopted to address real world issues in combination of optimization technologies as well as AI technologies.
*** NYK Group ESG Story
A guideline detailing concrete efforts to integrate ESG into management strategies of the NYK Group.
**** “Sail GREEN” project
A project being promoted by NYK’s Automotive Transport Headquarters to reduce CO2 emissions at all the transportation stages of vehicles and contribute to the eco-friendly supply chains of customers. The chief component of the project is a switch to LNG-fueled PCTCs, which emit less CO2 compared to conventional heavy-fueled vessels. In addition, reducing CO2 emissions at finished-car logistics terminals that NYK operates throughout the world, as well as during short sea and inland transportation, is also an important target of this project.
***** GRID aims to support activities for companies’ decarbonization while maximizing profit.
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