2024/07/15 更新

写真a

オウ ショ
王 緒
所属
理工学術院 創造理工学部
職名
講師(任期付)
学位
博士(工学) ( 2022年02月 早稲田大学 )

経歴

  • 2022年04月
    -
    継続中

    早稲田大学   創造理工学部 経営システム工学科   助教

  • 2022年04月
    -
    継続中

    神奈川大学   工学部 経営工学科   非常勤講師

  • 2023年04月
    -
    2024年03月

    芝浦工業大学   システム理工学部   非常勤講師

  • 2019年04月
    -
    2022年03月

    早稲田大学   創造理工学部 経営システム工学科   助手

所属学協会

  • 2018年04月
    -
    継続中

    日本経営工学会

  • 2016年04月
    -
    継続中

    日本オペレーションズリサーチ学会

研究分野

  • 社会システム工学

研究キーワード

  • 最適化

  • オペレーションズリサーチ

  • データ包絡分析法

受賞

  • Best Conference Paper Awards(Honourable Mention Award)

    2020年12月   IEEE IEEM2020  

    受賞者: Xu Wang, Takashi Hasuike

  • 学生奨励賞

    2018年05月   日本オペレーションズ・リサーチ学会研究部会「評価のOR」  

    受賞者: 王 緒

  • 学生優秀発表賞

    2017年11月   日本オペレーションズ・リサーチ学会 「東北ORセミナー:若手研究交流会」  

    受賞者: 王 緒

  • 研究科長賞

    2018年03月   東京理科大学  

 

論文

  • The Least-distance DEA Based Efficiency Improvement Under Multiple Perspectives

    Xu Wang, Takashi Hasuike

    2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)     818 - 823  2021年12月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

  • A Study on the Improvement Targets of Data Envelopment Analysis Models

    Xu Wang, Hiroki Iwamoto, Takashi Hasuike

    2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)    2023年12月

    DOI

  • Measuring China’s Energy Efficiency with Different DEA Models

    Xu Wang, Takashi Hasuike

    2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)     0995 - 0999  2022年12月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

  • Least-Distance Range Adjusted Measure in DEA: Efficiency Evaluation and Benchmarking for Japanese Banks

    Xu Wang, Takashi Hasuike

    Asia-Pacific Journal of Operational Research    2022年02月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

    Scopus

    2
    被引用数
    (Scopus)
  • A new approach on the lowest cost problem in data envelopment analysis

    Xu Wang, Kuan Lu, Takashi Hasuike

    Asian J. of Management Science and Applications   6 ( 1 ) 69 - 69  2021年  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

  • Least-distance Data Envelopment Analysis Model for Bankruptcy-based Performance Assessment

    Xu Wang, Takashi Hasuike

    2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)     235 - 239  2020年12月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

  • A New MIP Approach on the Least Distance Problem in DEA

    Xu Wang, Kuan Lu, Jianming Shi, Takashi Hasuike

    Asia-Pacific Journal of Operational Research    2020年05月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

    DOI

  • Cost Minimizing Target Setting Over the Whole Efficient Frontier in Data Envelopment Analysis

    Xu Wang, Takashi Hasuike

    Proceedings of 2019 Asian Conference of Management Science and Applications (ACMSA2019)     128 - 133  2019年10月  [査読有り]

    担当区分:筆頭著者, 最終著者, 責任著者

▼全件表示

講演・口頭発表等

  • The Least-distance DEA Based Efficiency Improvement Under Multiple Perspectives

    Xu Wang, Takashi Hasuike

    2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)  

    発表年月: 2021年12月

    開催年月:
    2021年12月
     
     
  • Least-Distance Range Adjusted Measure for Efficiency Evaluation and Benchmarking in DEA

    王緒, 蓮池隆

    日本オペレーションズ・リサーチ学会2020 年春季研究発表会  

    発表年月: 2021年03月

  • Least-distance Data Envelopment Analysis Model for Bankruptcy- based Performance Assessment

    Xu Wang, Takashi Hasuike

    2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM2020)  

    発表年月: 2020年12月

    開催年月:
    2020年12月
     
     
  • Cost Minimizing Target Setting Based on the Least Distance Model in DEA

    王 緒, 蓮池 隆

    日本経営工学会2020年春季大会  

    発表年月: 2020年03月

  • DEA Based Bankruptcy Assessment Approach

    Xu Wang, Takashi Hasuike

    2019 INFORMS Annual Meeting  

    発表年月: 2019年10月

    開催年月:
    2019年10月
     
     
  • Data Envelopment Analysis Based Financial Performance Evaluation and Bankruptcy Assessment

    王 緒, 蓮池 隆

    日本経営工学会2019年春季大会  

    発表年月: 2019年03月

  • The Least Distance Problem in Data Envelopment Analysis

    Xu Wang

    The 13th International Symposium on Operations Research and its Applications  

    発表年月: 2018年08月

  • A Branch and Bound Approach for the Least Distance Problem in DEA

    王 緒

    日本オペレーションズ・リサーチ学会研究部会「評価のOR」学生大会  

    発表年月: 2018年05月

  • A Method of Computing the Closest Efficient Projection Point in Data Envelopment Analysis

    王 緒

    日本オペレーションズ・リサーチ学会2018年春季研究発表会  

    発表年月: 2018年03月

  • A Method of Calculating Closest Efficient Projection in Data Envelopment Analysis

    王 緒

    日本オペレーションズ・リサーチ学会「東北ORセミナー:若手研究交流会」  

    発表年月: 2017年11月

▼全件表示

共同研究・競争的資金等の研究課題

  • 事業体に対する質保証の効率性測定や的確な改善方針提供を両立する動DEA手法の開発

    日本学術振興会  科学研究費助成事業 基盤研究(C)

    研究期間:

    2021年04月
    -
    2024年03月
     

    王 緒

     概要を見る

    DEAは評価対象の事業体(Decision Making Unit, DMU)が効率的であるかどうかを判断するだけではなく、非効率的なDMUに対し、効率値と改善目標が両方与えられる。しかし、従来のDEAモデルでは、評価対象のDMUの効率性を改善するため、最大の改善量の改善目標を提供している。結局、提供される改善目標は評価対象のDMUから遠く離れ、実現するのが困難であるとしばしば批判されている。DEAの役割は単に効率性を測るだけではなく、効率性を改善する方法も提案できることが重要である。そういった観点から、最小の改善量で効率化を実現するモデルは最適であると考えられる。そこで、本研究では、近年盛んに研究されている最短距離DEAに注目し、より実用的な新最短距離DEAモデルの構築を目指している。
    DEAでは、ほとんどの場合が複数の入力と出力となっている。最短距離DEAモデルは評価対象のDMUと改善目標の間の距離を最小化できるが、課題の一つは改善方向ごとに改善コストが異なる可能性があるため、距離が一番近くても、改善にかかる総コストが一番低いとは限らない。DEAの中の多くのモデルに関して、従来のモデルや最短距離DEAモデルはcost-blind(改善目標を実現するためにかかるコストのことを考慮されていない)である。しかし、DMUにとっては、なるべく改善コストを抑えたい。そこで、非効率的なDMUの効率性の改善を行う際に、改善目標の総実現コストを最小化する手法の開発が必要であると考えられる。今年度では、研究代表者の先行研究の考え方・成果を拡張することにより、改善目標の総実現コストの最小化を行った。具体的に、simulated datasetsを生成し、これらのデータセットを用いて、数値実験により、改善目標の総実現コストを最小化できる提案した手法の有効性を検証した。それと並行し、関連文献を調査し、提案手法の改善を検討した。

Misc

 

現在担当している科目

担当経験のある科目(授業)

  • 経営工学実験実習I

    神奈川大学  

    2022年04月
    -
    継続中
     

  • 線形代数I

    芝浦工業大学  

    2023年04月
    -
    2024年03月
     

  • オペレーションズ・リサーチII

    神奈川大学  

    2022年04月
    -
    2024年03月
     

  • 理工学基礎実験1A

    早稲田大学  

  • 基礎オペレーションズリサーチ演習(補助)

    早稲田大学  

  • 理工学基礎実験1B

    早稲田大学  

▼全件表示

 

特定課題制度(学内資金)

  • 最短距離DEAモデル関する実証研究(その2)

    2023年  

     概要を見る

    Given the inherent benchmarking capability of DEA against best practices, we focused primarily on comparing the improvement targets generated by two DEA models: the conventional additive (ADD) model and the least-distance model. This analysis is performed using a time-series dataset comprising 86 retail companies in Japan. The main contributions of this study are summarized as follows.We conduct a comparative analysis between the improvement targets generated by the conventional ADD and the least-distance models.We investigate the effectiveness of the improvement targets generated by the two types of models by evaluating the efficiency of these targets in the subsequent year.Based on the results of the numerical experiments and analysis, it appears that the easy-to-achieve improvement targets generated by the least-distance model demonstrate higher effectiveness.Further exploration of improvement targets generated by other variations of the least-distance DEA models would be beneficial through additional numerical experiments using real-world or simulated data. Additionally, conducting empirical studies by applying the least-distance DEA models to diverse fields for efficiency improvement would contribute to the existing body of research.

  • 最短距離DEAモデルに関する実証研究

    2022年  

     概要を見る

    The efficacy of DEA in efficiency measurement is the primary reason why DEA has gained significant attentions from researchers across the world. The primary benefits of DEA include its ability to provide both efficiency scores and improvement targets for decision making units(DMUs) under measurement. The improvement targets suggest several ways to improve inefficient DMUs’ efficiency. An improvement target that is close to the DMU under measurement is considered to be easy-to-achieve in DEA. However, in previous studies, most conventional DEA models used for China’s efficiency measurement provided a far improvement targets that cannot be achieved immediately and would require several years. Thus, a least-distance DEA model that can provide a closer improvement target is used in our study. Furthermore, a conventional DEA model and a ratio type DEA model are used to study and compare the performances. All three DEA models are applied to the measurement of China’s energy efficiency in 1997, 2002, 2007, and 2012. The differences in the efficiency scores and improvement targets provided by the three models have been reviewed. Although the results show different improvement targets, it can be inferred that reducing the overall energy consumption and increasing the GDP are still two effective measures for inefficient provinces, districts, and cities according to the experimental results. 

  • 最短距離DEAに基づく複数の評価視点より効率性評価手法の開発

    2021年  

     概要を見る

    Data envelopment analysis (DEA) is widely used to evaluate and improve the relative efficiency of decision making units (DMUs), which have multiple inputs and outputs. However, traditional DEA models can only handle a single perspective. We proposed a new approach for efficiency improvement under multiple perspectives based on the least-distance DEA.  The Nash bargaining game (NBG) theory has been used in extant studies to avoid conflicts and obtain a rational direction of efficiency improvement under multiple perspectives. Because of the practicality of the closest efficient target, we first proposed a least-distance DEA model incorporating NBG. A numerical experiment is conducted to compare the performance of our proposed approach with that of previous studies. The results reveal that our proposed approach can (1) evaluate the efficiency of DMUs under multiple perspectives, and (2) provide more easy-to-achieve efficiency improvement suggestions for the assessed DMUs. Thus, the proposed approach has remarkable potential applicability in decision making.

  • 非効率的な事業体に最適な改善経路を提供する動的なDEA手法の開発

    2021年  

     概要を見る

    Data envelopment analysis(DEA) has been widely applied to evaluate relative efficiency and provide benchmarking information(efficient target) for decision making units(DMUs). Recently, the least-distance DEA has been extensively researched, and various corresponding models are proposed because of the practicability of the least-distance benchmarking information(closest efficient target). We have formulated the least-distance range adjusted measure (LRAM), which satisfies a set of desirable properties, as a new practical DEA model for efficiency evaluation and benchmarking. Based on more numerical experiments and deeper analysis, we found (1) that efficient targets provided by the LRAM match the evaluated DMUs more closely than those provided by the convention range adjusted measure(RAM) for most of the inputs and outputs, (2) although LRAM may suggest modifying a greater number of inputs and outputs than that suggested by the RAM, it optimizes the input-output modification to significantly reduce the total percentages of modifications required for each of the inefficient banks to achieve efficiency. Thus, the LRAM suggests the required modifications to achieve efficiency in a more equitable and balanced manner.

  • 最短距離データ包絡分析法の理論及び応用に関する研究

    2020年  

     概要を見る

    Dataenvelopment analysis (DEA) introduced in 1978 has been widely applied toevaluating the relative efficiency and providing efficient target for decisionmaking units (DMUs). The conventional range adjusted measure (RAM) in DEA actsas a well-defined measure satisfying a set of desirable properties, especiallythe strong monotonicity. However, because of the practicality of the closestefficient target, we focus on formulating the least-distance range adjustedmeasure (LRAM) and proposing the use of an efficient mixed integer programming(MIP) approach to compute it. Our formulated LRAM: (1) satisfies the desirableproperties as the conventional RAM; (2) provides the least-distance benchmarkinginformation for inefficient DMUs, which will make the efficiency improvementeasy, and (3) can be computed easily by using the proposed MIP approach. Here, weapply the LRAM to a Japanese banking data set corresponding to the period 2017-2019.Based on the results: the LRAM generates higher efficiency scores and allowsinefficient banks to improve their efficiency with a smaller extent ofinput-output modification than that required by the RAM, which indicates thatthe LRAM can provide more easy-to-achieve benchmarking information for inefficientbanks. Therefore, from the perspective of the managers of DMUs, we provide a valuableLRAM for efficiency evaluation and benchmarking analysis.

  • DEAに基づく最小実現コストの改善目標設定アプローチに関する研究

    2020年  

     概要を見る

    DataEnvelopment Analysis(DEA) has been widely used as a means of relativeefficiency evaluation since the first DEA model was introduced in 1978. It uses mathematicalprogramming techniques and models to evaluate the relative efficiency of decisionmaking units(DMUs) with multiple inputs and outputs. In DEA, an inefficient DMU’sefficiency can be improved by adjusting the inputs or outputs or both to reachthe projection target on the efficient frontier. In this research, we aim atsolving the lowest cost problem in DEA, which is to provide an efficient targetfor an inefficient DMU with the lowest adjustment costs. For this purpose, anew approach based on the least distance DEA model is proposed. Here, themarginal costs of adjusting the inputs and outputs are assumed to be known andsymmetrical. For the practical merit, different with the existing studies, our approachis able to increase inputs and decrease outputs. Numerical experiments are conductedto compare the performance of the proposed approach with previous existingstudies. The results show that the proposed approach can always provide anefficient target with no higher total adjustment costs than the costs oftargets provided by previous approaches. Therefore, this research’scontributions can be summarized as follows:  • Propose an approach to DEA that minimizes the totaladjustment costs incurred when transitioning an inefficient DMU to an efficienttarget;• Enable the real world condition that some inputs couldbe increased or some outputs could be reduced to be reflected in the targetsetting process.Thus, the proposed approach is more practical and usefulfor decision makers.

  • DEAに基づく新たなベンチマーキングの手法の理論構築と実践に関する研究

    2019年  

     概要を見る

    The technique ofdata envelopment analysis (DEA) introduced by Charnes, Cooper and Rhodes (CCR)in 1978 has been widely applied to evaluating the relative efficiency ofdecision making units (DMUs). DEA provides not only efficient performance ofeach assessed DMU but also a target that improves efficiency of the DMU. Theefficient targets provided by the classical DEA models are always very far fromthe assessed DMU. However, the closest efficient target is often moreappropriate because it needs less effort to make the DMU efficient from theperspective of managers of DMUs. The difficulties of computing the closestefficient target are: (a) the definition of the efficient frontier is given inan implicit fashion, that is hard to be exploited in an algorithm; (b) theefficient frontier is nonconvex. In our research, in order to overcome thesedifficulties, we use the optimization tool (Karush-Kuhn-Tucker conditions) totransform the definition of the efficient frontier and make the definitioncomputation-friendly. The main works we have done can be summarized as follows.(1) We proposed anew approach that can provide an efficient target that is closer to theassessed DMU than that provided by the existing studies;(2) We used theproposed approach in (1) to assess the bankruptcy-based performance of Japanesebanks. Then, an early warning of the firm's financial performance and an easy-to-achieveimprovement plan for the default firm can be provided.

▼全件表示