A joint chance-constrained data envelopment analysis model with random output data

被引:5
|
作者
Shiraz, Rashed Khanjani [1 ]
Tavana, Madjid [2 ,3 ,5 ]
Fukuyama, Hirofumi [4 ]
机构
[1] Univ Tabriz, Dept Appl Math, Tabriz, Iran
[2] La Salle Univ, Business Syst & Analyt Dept, Distinguished Chair Business Syst & Analyt, Philadelphia, PA 19141 USA
[3] Univ Paderborn, Fac Business Adm & Econ, Business Informat Syst Dept, Paderborn, Germany
[4] Fukuoka Univ, Fac Commerce, Fukuoka, Japan
[5] La Salle Univ, Business Syst & Analyt Dept, Distinguished Chair Business Analyt, Philadelphia, PA 19141 USA
关键词
Data envelopment analysis; Joint chance-constrained programming; Random data; Second-order cone programming; STOCHASTIC DEA; EFFICIENCY; REPRESENTATION; EQUIVALENTS; INPUT; RISK;
D O I
10.1007/s12351-019-00478-0
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
Data envelopment analysis (DEA) is a mathematical programming approach for evaluating the technical efficiency performances of a set of comparable decision-making units that transform multiple inputs into multiple outputs. The conventional DEA models are based on crisp input and output data, but real-world problems often involve random output data. The main purpose of the paper is to propose a joint chance-constrained DEA model for analyzing a real-world situation characterized by random outputs and crisp inputs. After developing the model, we carry out the following: First, we obtain an upper bound of this stochastic non-linear model deterministically by applying a piecewise linear approximation algorithm based on second-order cone programming; Second, we obtain a lower bound with use of a piecewise tangent approximation algorithm, which is also based on second-order cone programming; and then we use a numerical example to demonstrate the applicability of the proposed joint chance-constrained DEA framework.
引用
收藏
页码:1255 / 1277
页数:23
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