Updated on 2022/12/08

写真a

 
WANG, Xu
 
Scopus Paper Info  
Paper Count: 0  Citation Count: 0  h-index: 1

Citation count denotes the number of citations in papers published for a particular year.

Affiliation
Faculty of Science and Engineering, School of Creative Science and Engineering
Job title
Assistant Professor(without tenure)

Degree

  • 2018.03   東京理科大学   修士

  • 2022.02   Waseda University   博士(工学)

Professional Memberships

  • 2018.04
    -
    Now

    JAPAN INDUSTRIAL MANAGEMENT ASSOCIATION

  • 2016.04
    -
    Now

    日本オペレーションズリサーチ学会

 

Research Areas

  • Social systems engineering

Research Interests

  • Optimization

  • Operations Research

  • Data Envelopment Analysis

Papers

  • 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)    2021.12  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    DOI

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

    Xu Wang, Takashi Hasuike

       2022.02  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    DOI

    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  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    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  2020.12  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    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    2020.05  [Refereed]

    Authorship:Lead author, Last author, Corresponding author

    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  2019.10

    Authorship:Lead author, Last author, Corresponding author

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Misc

  • Least-distance Range Adjusted Measureに基づく効率性評価およびベンチマーキング—特集 将来を担う若手研究者たち(その1)

    王 緒

    オペレーションズ・リサーチ = Communications of the Operations Research Society of Japan : 経営の科学   67 ( 7 ) 372 - 376  2022.07

Awards

  • Honourable Mention Award

    2020.12   International Conference on Industrial Engineering and Engineering Management   Least-distance Data Envelopment Analysis Model for Bankruptcy-based Performance Assessment

  • 学生奨励賞

    2018.04   日本オペレーションズ・リサーチ学会研究部会「評価のOR」  

    Winner: 王 緒

  • 研究科長賞

    2018.02   東京理科大学 経営学研究科  

    Winner: 王 緒

  • Student Best Presentation Award

    2017.11  

    Winner: WANG XU

Research Projects

  • 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)

    Project Year :

    2021.04
    -
    2024.03
     

    王 緒

Presentations

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

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

    Presentation date: 2021.12

    Event date:
    2021.12
     
     
  • Least-Distance Range Adjusted Measure for Efficiency Evaluation and Benchmarking in DEA

    Presentation date: 2021.03

  • 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) 

    Presentation date: 2020.12

    Event date:
    2020.12
     
     
  • 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 

    Presentation date: 2020.03

  • DEA Based Bankruptcy Assessment Approach

    Presentation date: 2019.10

    Event date:
    2019.10
     
     
  • 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 

    Presentation date: 2019.03

  • The Least Distance Problem in Data Envelopment Analysis

    WANG XU

    ISORA 2018 

    Presentation date: 2018.08

  • 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 

    Presentation date: 2018.05

  • 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 

    Presentation date: 2018.03

  • 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」 

    Presentation date: 2017.11

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Specific Research

  • 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  

     View Summary

    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  

     View Summary

    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.

  • 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  

     View Summary

    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.

  • 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  

     View Summary

    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  

     View Summary

    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.

 

Teaching Experience

  • 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

    Waseda University