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An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: a transportation system application

  • Big Data Analytics in Operations & Supply Chain Management
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Abstract

In the big data context, decision makers usually face the problem of evaluating environmental efficiencies of a massive number of decision making units (DMUs) using the data envelopment analysis (DEA) method. However, standard implementations of the traditional DEA calculation process will consume much time when the data set is very large. To eliminate this limitation of DEA applied to big data, firstly, the slacks-based measure (SBM) model is extended considering undesirable outputs and the variable returns to scale (VRS) assumption for environmental efficiency evaluation of the DMUs. Then, an approach comprised of two algorithms is proposed for environmental efficiency evaluation when the number of DMUs is massive. The set of DMUs is partitioned into subsets, a technique which facilitates the application of a parallel computing mechanism. Algorithm 1 can be used for identifying the environment efficient DMUs in any DMU set. Further, Algorithm 2 (a parallel computing algorithm) shows how to use the proposed model and Algorithm 1 in parallel to find the environmental efficiencies of all DMUs. A simulation shows that the parallel computing design helps to significantly reduce calculation time when completing environmental efficiency evaluation tasks with large data sets, compared with using the traditional calculation processes. Finally, the proposed approach is applied to do environmental efficiency analysis of transportation systems.

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Notes

  1. This mechanism is an easy way to do parallel computing. The whole efficiency evaluation task is separated into ten parts, and each computer does one part of the ten independently. Each computer contains all the DMUs but it only calculates efficiency for its assigned part of the DMU set. All the computers work concurrently and the final calculation time will be no larger than the time needed by the last computer to complete its assigned task.

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Acknowledgments

The research is supported by the National Natural Science Funds of China (Nos. 71222106, 71110107024, 71171001, 71471001, and 71501139), Research Fund for the Doctoral Program of Higher Education of China (No. 20133402110028), Foundation for the Authors of National Excellent Doctoral Dissertation of P. R. China (No. 201279), Fundamental Research Funds for the Central Universities (No. WK2040160008), Top-Notch Young Talents Program of China, and Internet of Things Industry Development Research Base Biding Project, Nanjing University of Posts and Telecommunications (No. JDS215005).

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Authors and Affiliations

  1. School of Management, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Jun-Fei Chu & Jie Wu

  2. Collaborative Innovation Center for Ecological Economics and Management, Anhui University of Finance and Economics, Bengbu, 233030, Anhui, China

    Ma-Lin Song

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  1. Jun-Fei Chu
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Correspondence to Ma-Lin Song.

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Chu, JF., Wu, J. & Song, ML. An SBM-DEA model with parallel computing design for environmental efficiency evaluation in the big data context: a transportation system application. Ann Oper Res 270, 105–124 (2018). https://doi.org/10.1007/s10479-016-2264-7

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