TY - JOUR
T1 - Determining radial efficiency with a large data set by solving small-size linear programs
AU - Chen, Wen-Chih
AU - Lai, Sheng Yung
PY - 2017/3/1
Y1 - 2017/3/1
N2 - This paper presents a new algorithm for determining radial efficiency with a large data set by using small-size linear programs (LPs). Instead of trying to “reduce” the size of individual LPs, the proposed algorithm attempts to “control” the size of individual LPs, e.g., no more than 100 data points each time while maintaining the solution quality. The algorithm is specifically designed to address the problem of LP size limitation. From the empirical results, we conclude that the proposed algorithm can converge within a reasonable number of iterations without incurring extra computation time and has savings of up to 60 % of the benchmarks when the data set contains 15,000 points.
AB - This paper presents a new algorithm for determining radial efficiency with a large data set by using small-size linear programs (LPs). Instead of trying to “reduce” the size of individual LPs, the proposed algorithm attempts to “control” the size of individual LPs, e.g., no more than 100 data points each time while maintaining the solution quality. The algorithm is specifically designed to address the problem of LP size limitation. From the empirical results, we conclude that the proposed algorithm can converge within a reasonable number of iterations without incurring extra computation time and has savings of up to 60 % of the benchmarks when the data set contains 15,000 points.
KW - Data envelopment analysis
KW - Large-scale computation
KW - Radial efficiency
UR - http://www.scopus.com/inward/record.url?scp=84939826321&partnerID=8YFLogxK
U2 - 10.1007/s10479-015-1968-4
DO - 10.1007/s10479-015-1968-4
M3 - Article
AN - SCOPUS:84939826321
VL - 250
SP - 147
EP - 166
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
IS - 1
ER -