| Message | Many decision-making problems in the real world can be formulated as combinatorial optimization problems,
and various optimization methods have been proposed to solve them.
However, most optimization approaches are designed on the assumption that the input data as well as the model are known and accurate.
On the other hand, several optimization problems arising in real world applications do not have accurate estimates of the problem parameters or mathematical models when the optimization decision is taken.
In order to solve such problems, I aim to propose new mathematical optimization models and algorithms for combinatorial optimization problems under uncertainties,and confirm the performance by using real-world instances.
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