A novel Data Envelopment Analysis model for evaluating industrial production and environmental management system

Abstract Industrial production and environmental management systems should be simultaneously considered for sustainable development. This paper evaluated the performance of an integrated two-stage system using a proposed type-2 fuzzy bi-objective two-stage slacks-based measurement Data Envelopment Analysis model with super efficiency. A Step Method was applied to solve for the Pareto optimal solution to ensure no implicit priority was given to one stage over the other, and a CV-based reduction method and generalized credibility based chance constrained programming were used to cope with the type-2 fuzzy variables. A case study in China was then developed from a time-perspective and a region-perspective, the results from which indicated that the overall performance of China's integrated system improved from 2005 to 2014, and the efficiency gap between the industrial production system and the environmental management system reduced, however, there was significant disparity shown across the different economic regions. Three comparative analyses were then conducted to highlight the superiority of the proposed model. The developed model was able to: measure efficiency scores and find proportionate ratios and disproportionate slacks for each DMU to decrease inputs for performance improvement, distinguish the DMU from DMUs with same efficiency value and indicate the maximum change scope for the inputs and outputs to maintain the DMU efficiency. In addition, type-2 fuzzy sets were incorporated to describe the fuzziness with greater flexibility, which can assist decision makers and produce more accurate, robust results.

[1]  Liang Chen,et al.  Environmental efficiency analysis of China's regional industry: a data envelopment analysis (DEA) based approach , 2017 .

[2]  Kuangnan Fang,et al.  Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA , 2015 .

[3]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[4]  Tao Zhao,et al.  Environmental assessment and investment strategies of provincial industrial sector in China — Analysis based on DEA model , 2016 .

[5]  Yiwen Bian,et al.  Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach , 2015 .

[6]  Manoranjan Maiti,et al.  Fixed charge transportation problem with type-2 fuzzy variables , 2014, Inf. Sci..

[7]  Hong Li,et al.  Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs , 2014 .

[8]  Kaoru Tone,et al.  A slacks-based measure of super-efficiency in data envelopment analysis , 2001, Eur. J. Oper. Res..

[9]  Chiang Kao,et al.  Fuzzy efficiency measures in data envelopment analysis , 2000, Fuzzy Sets Syst..

[10]  J. Bi,et al.  Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach , 2008 .

[11]  Toshiyuki Sueyoshi,et al.  China's regional sustainability and diversified resource allocation: DEA environmental assessment on economic development and air pollution , 2015 .

[12]  Joe Zhu,et al.  Network DEA: Additive efficiency decomposition , 2010, Eur. J. Oper. Res..

[13]  Joe Zhu,et al.  Additive efficiency decomposition in two-stage DEA , 2009, Eur. J. Oper. Res..

[14]  Yian-Kui Liu,et al.  Methods of critical value reduction for type-2 fuzzy variables and their applications , 2011, J. Comput. Appl. Math..

[15]  Lawrence M. Seiford,et al.  Modeling undesirable factors in efficiency evaluation , 2002, Eur. J. Oper. Res..

[16]  Shiv Prasad Yadav,et al.  A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India , 2014, Expert Syst. Appl..

[17]  Dimitris K. Despotis,et al.  A network DEA approach for series multi-stage processes☆ , 2016 .

[18]  Alireza Amirteimoori,et al.  Two-stage network structures with undesirable outputs: A DEA based approach , 2014 .

[19]  Chiang Kao,et al.  Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan , 2008, Eur. J. Oper. Res..

[20]  Lazim Abdullah,et al.  Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management , 2015, Expert Syst. Appl..

[21]  Somchai Pathomsiri,et al.  Impact of undesirable outputs on the productivity of US airports , 2008 .

[22]  Wenbin Liu,et al.  DEA Models via Goal Programming , 1999 .

[23]  P. Andersen,et al.  A procedure for ranking efficient units in data envelopment analysis , 1993 .

[24]  Shahaboddin Shamshirband,et al.  Developing a fuzzy clustering model for better energy use in farm management systems , 2015 .

[25]  Ainuddin Wahid Abdul Wahab,et al.  A multi-objective evolutionary algorithm for energy management of agricultural systems—A case study in Iran , 2015 .

[26]  Witold Pedrycz,et al.  Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation , 2016, Appl. Soft Comput..

[27]  Qunwei Wang,et al.  Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach , 2016 .