Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique
Xie, Jianhui1; Xie, Qiwei2,3; Li, Yongjun4; Liang, Liang5
发表期刊ANNALS OF OPERATIONS RESEARCH
ISSN0254-5330
2021-04-12
页码28
通讯作者Li, Yongjun(lionli@ustc.edu.cn)
摘要The majority of data envelopment analysis (DEA) models can be linearized via the classical Charnes-Cooper transformation. Nevertheless, this transformation does not apply to sum-of-fractional DEA efficiencies models, such as the secondary goal I (SG-I) cross efficiency model and the arithmetic mean two-stage network DEA model. To solve a sum-of-fractional DEA efficiencies model, we convert it into bilinear programming. Then, the obtained bilinear programming is relaxed to mixed-integer linear programming (MILP) by using a multiparametric disaggregation technique. We reveal the hidden mathematical structures of sum-of-fractional DEA efficiencies models, and propose corresponding discretization strategies to make the models more easily to be solved. Discretization of the multipliers of inputs or the DEA efficiencies in the objective function depends on the number of multipliers and decision-making units. The obtained MILP provides an upper bound for the solution and can be tightened as desired by adding binary variables. Finally, an algorithm based on MILP is developed to search for the global optimal solution. The effectiveness of the proposed method is verified by using it to solve the SG-I cross efficiency model and the arithmetic mean two-stage network DEA model. Results of the numerical applications show that the proposed approach can solve the SG-I cross efficiency model with 100 decision-making units, 3 inputs, and 3 outputs in 329.6 s. Moreover, the proposed approach obtains more accurate solutions in less time than the heuristic search procedure when solving the arithmetic mean two-stage network DEA model.
关键词Data envelopment analysis Mixed-integer linear programming Global optimal solution Fractional programming
DOI10.1007/s10479-021-04026-y
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[71701220] ; National Natural Science Foundation of China[72071192] ; National Natural Science Foundation of China[71671172] ; National Natural Science Foundation of China[71631006] ; Natural Science Foundation of Beijing[9202002] ; GreatWall Scholar Training Program of Beijing Municipality[CITTCD20180305] ; Social Science Foundation of Beijing[16JDGLC005]
项目资助者National Natural Science Foundation of China ; Natural Science Foundation of Beijing ; GreatWall Scholar Training Program of Beijing Municipality ; Social Science Foundation of Beijing
WOS研究方向Operations Research & Management Science
WOS类目Operations Research & Management Science
WOS记录号WOS:000639514600005
出版者SPRINGER
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/44357
专题脑图谱与类脑智能实验室_微观重建与智能分析
通讯作者Li, Yongjun
作者单位1.Sun Yat Sen Univ, Int Sch Business & Finance, Zhuhai 519082, Guangdong, Peoples R China
2.Beijing Univ Technol, Res Base Beijing Modern Mfg Dev, Beijing 100124, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Univ Sci & Technol China, Sch Management, Hefei 230026, Anhui, Peoples R China
5.Hefei Univ Technol, Sch Management, 193 TunXi Rd, Hefei 230009, Anhui, Peoples R China
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GB/T 7714
Xie, Jianhui,Xie, Qiwei,Li, Yongjun,et al. Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique[J]. ANNALS OF OPERATIONS RESEARCH,2021:28.
APA Xie, Jianhui,Xie, Qiwei,Li, Yongjun,&Liang, Liang.(2021).Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique.ANNALS OF OPERATIONS RESEARCH,28.
MLA Xie, Jianhui,et al."Solving data envelopment analysis models with sum-of-fractional objectives: a global optimal approach based on the multiparametric disaggregation technique".ANNALS OF OPERATIONS RESEARCH (2021):28.
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