Shunji Umetani

Research interest

Mathematical optimization gives us powerful solutions to tackle a wide variety of real applications with large-scale data. However, most of these problems are classified into the class of NP-hard from the view of computational complexity. Under the background, we are aiming to develop practical algorithms for harder and larger combinatorial optimization problems within reasonable computation time.

The current research topics are as follows:

• Automatic algorithm configuration to improve the performance of general-purpose heuristic solvers

• Heuristic algorithms for large-scale combinatorial optimization problems

• Packing and cutting problems and their applications

• Mathematical optimization models and algorithms for solving real applications

News

• 2024/9/25, Our paper has been accepted for publication in The Review of Socionetwork Strategies (RSS).
  Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization

• 2024/5/24, We have uploaded the following preprint.
  S.Ikeda, N.Nishimura and S.Umetani, Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization, arXiv preprints, arXiv: 2405.14909, 2024. paper

• 2023/10/1, I have moved to Advanced Technology Lab., Recruit Co., Ltd.

• 2022/11/1, We have uploaded the following preprint.
  H.Masuyama, H.Dan and S.Umetani, Curse of scale freeness: Intractability of large-scale combinatorial optimization with multi-start methods, arXiv preprints, arXiv:2210.16678, 2022. paper

• 2022/3/18, Our paper has been accepted for publication in European Journal of Operational Research (EJOR).
  Coordinate descent heuristics for the irregular strip packing problem of rasterized shapes

• 2021/9/21, We have sent a press release about collaboration with TOKYO-Mitsui O.S.K. Lines, Ltd.
  MOL Group Adopts Car Carrier Operation Digital Transformation Promotion Project 'Mathematical Optimization': Part II  Improving Customer Satisfaction and Reducing Environmental Impact with More Efficient Cargo Loading Plans  press release

• 2021/5/28, We have sent a press release about collaboration with TOKYO-Mitsui O.S.K. Lines, Ltd.
  MOL accelerates Digital Transformation by introducing Support System for Car Carrier Allocation Planning  press release

• 2021/4/13, We have uploaded the following preprint.
  S.Umetani and S.Murakami, Coordinate descent heuristics for the irregular strip packing problem of rasterized shapes, arXiv preprints, arXiv:2104.04525, 2021. paper

• 2020/10/1, The endowed research laboratory in mathematical Optimization has been established, donated by BrainPad Inc. and Fujitsu Laboratories Ltd.

• 2020/5/6, Our paper has been published in the Journal of the Operations Research Society of Japan (JORSJ).
  N.Uematsu, S.Umetani and Y.Kawahara, An efficient branch-and-cut algorithm for submodular function maximization, Journal of the Operations Research Society of Japan, 63 (2020), 41-59. DOI: 10.15807/jorsj.63.41 (open access) supplement 

• 2019/10/1, We have sent a press release about collaboration with TOKYO-Mitsui O.S.K. Lines, Ltd.
  MOL Group Successfully Develops Car Carrier Allocation/Loading Plan with Fundamental technologies of AI — Significantly Improving Efficiency in Planning and Validation with 'Mathematical Optimization' — press release 

• 2019/8/5, Our paper has been accepted for publication in the Journal of the Operations Research Society of Japan (JORSJ).
  An efficient branch-and-cut algorithm for submodular function maximization

• 2019/6/23, Our paper has been accepted for publication at IEEE SMC 2019 Conference.
  An efficient branch-and-cut algorithm for approximately submodular function maximization

Contact

E-mail: umetani@ieee.org