2021 Fiscal Year Final Research Report
Staff scheduling: Development of information-providing technologies for effective use of optimization algorithms in the field
| Project/Area Number |
19K11843
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 60020:Mathematical informatics-related
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| Research Institution | Seikei University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
伊藤 靖彦 成蹊大学, 理工学部, 研究員 (50838993)
加藤 晴康 成蹊大学, 理工学部, 研究員 (60838994)
呉 偉 静岡大学, 工学部, 助教 (90804815)
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| Project Period (FY) |
2019-04-01 – 2022-03-31
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| Keywords | スケジューリング / 最適解の分布 / 多様性と類似性 / 公平性 / ロバスト |
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| Outline of Final Research Achievements |
We aimed to provide information for reflecting latent constraints and priorities in nurse scheduling, considering the distribution of optimal solutions, fairness and robustness.
We used value differences in decision variables appearing frequently in the formulation constraints as a diversity measure and created models that provide diverse solutions. We defined two types of transitions between solutions and observed the solution quality in the transition process. In distance-based transitions, we efficiently obtained many optimal solutions. Information from integrating diverse and similar solutions allowed efficient modifications of solutions.In soccer league matches, to fairly balance each team’s load, we proposed an algorithm to minimize the total carry-over effect. For robustness, we proposed an iterated dual substitution method for a min-max regret 0-1 integer programming model that can represent scheduling problems with uncertainty and implemented a python solver using this method.
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| Free Research Field |
数理最適化
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| Academic Significance and Societal Importance of the Research Achievements |
人が持つ暗黙知，言語化していない制約や評価尺度が存在する問題では，最適化アルゴリズムの現場利用が難しい．最適化技術（モデルやアルゴリズム）が現場で真に利用されるためには，「暗黙知をうまく反映・吸収すること」を支援できる情報が必要である．本研究は，最適解の分布（多様性と類似性）を提供して，現場における解の選択，修正，評価の可能性を広げる．加えて，解が与える公平性，解のロバスト性が，現場の意思決定を助ける．最適化研究においては，最適解を1つ得ることが一般的であり，最適解間の関係に主眼を置いた研究は少ない．多様性と類似性の視点を持って分布を議論することはユニークな試み（学術的な問い）だと考える．
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