日本語 
Nippon Steel Solutions Chubu Co., Ltd., a group company of Nippon Steel Solutions, will begin offering a new service called “OptiMagic” that utilizes mathematical optimization technology and generative AI. This service will work with customers to identify and organize potential and apparent issues in their daily planning operations, and guide them toward solutions using a mathematical approach.
As an expert in optimized implementation
To date, the Nippon Steel Solutions Group has provided mathematical optimization support in a wide range of fields, from supply chain management including demand forecasting, production planning, transportation planning, and personnel allocation planning, to creating match schedules for professional sports leagues. In the Chubu region, we have been involved in the construction of numerous large-scale optimization models for industries that form the backbone of Japan, such as steel and automobiles, and have gained experience as experts in optimization implementation.
Characteristics and challenges of mathematical optimization
The advantage of mathematical optimization lies in its ability to model problems mathematically, even under constraints, and derive the most efficient and optimal solution (combination or plan). These mathematical optimization techniques have evolved alongside advancements in operations research (OR) and computers.
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Mathematical optimization is increasingly being applied, particularly in planning-related tasks within companies. It is widely used to analyze complex organizational challenges such as resource allocation, scheduling, and logistics, leveraging mathematics, statistics, and algorithms to support more rational decision-making. Another characteristic is its high reproducibility and reliability, as it obtains optimal solutions based on clear conditions and numerical values, providing supporting evidence for the results.
On the other hand, building optimization models requires iterative work from requirements definition to implementation, which is a challenge due to the significant time and effort involved. Furthermore, model construction and algorithm selection require specialized knowledge in mathematics and computer science, making it difficult for on-site personnel to handle alone. Against this backdrop, the systematization of mathematical optimization has so far been mainly adopted by large companies that can easily secure sufficient resources and specialized personnel.
SOURCE: PRTimes
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