DEA based production planning considering technology heterogeneity with undesirable outputs

Many researchers have concentrated on production planning issues by using data envelopment analysis (DEA). However, the assumption made by existing approaches that all decision making units (DMUs) are equipped with the same level of production technology is not realistic. Additionally, with the development in the society, environmental factors have come to play important roles in the production process as well. Thus, undesirable outputs should be considered in production planning problems. Therefore, this paper considers the technology heterogeneity factors and undesirable outputs using the data envelopment analysis-based production planning approach. Two examples containing a numerical example that compare with other method and a real sample that concerns the industrial development of 30 provinces in China are used to validate the applicability of our approach.

[1]  C.A.K. Lovell,et al.  Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach , 1989 .

[2]  Ana S. Camanho,et al.  Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis , 2015, Eur. J. Oper. Res..

[3]  Sebastián Lozano,et al.  Network DEA approach to airports performance assessment considering undesirable outputs , 2013 .

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

[5]  Yan Luo,et al.  Environmental performance analysis of Chinese industry from a slacks-based perspective , 2015, Ann. Oper. Res..

[6]  Peng Zhou,et al.  A survey of data envelopment analysis in energy and environmental studies , 2008, Eur. J. Oper. Res..

[7]  A. Hailu,et al.  Non‐Parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry , 2001 .

[8]  George E. Battese,et al.  Technology Gap, Efficiency, and a Stochastic Metafrontier Function , 2002 .

[9]  Y. Geng,et al.  Efficient allocation of CO2 emissions in China: a zero sum gains data envelopment model , 2016 .

[10]  Xiongfeng Pan,et al.  Spatial club convergence of regional energy efficiency in China , 2015 .

[11]  Diego Prior,et al.  On centralized resource utilization and its reallocation by using DEA , 2014, Ann. Oper. Res..

[12]  Alireza Amirteimoori,et al.  Production planning: a DEA-based approach , 2011 .

[13]  Behrouz Arabi,et al.  A new slacks-based measure of Malmquist–Luenberger index in the presence of undesirable outputs , 2015 .

[14]  Zhongbao Zhou,et al.  Two-stage DEA models with undesirable input-intermediate-outputs $ , 2015 .

[15]  Jie Wu,et al.  Two-Stage Network Structures with Undesirable Intermediate Outputs Reused: A DEA Based Approach , 2015 .

[16]  Rui Zhang,et al.  DEA-based production planning considering influencing factors , 2015, J. Oper. Res. Soc..

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

[18]  S. Lozano,et al.  Centralized Resource Allocation Using Data Envelopment Analysis , 2004 .

[19]  Gongbing Bi,et al.  DEA-based production planning , 2010 .

[20]  F. Hosseinzadeh Lotfi,et al.  DEA-Based Production Planning Changes in General Situation , 2010 .

[21]  Rolf Färe,et al.  Environmental production functions and environmental directional distance functions , 2007 .

[22]  Harald Dyckhoff,et al.  Measuring ecological efficiency with data envelopment analysis (DEA) , 2001, Eur. J. Oper. Res..

[23]  Alireza Amirteimoori,et al.  Production planning in data envelopment analysis , 2012 .

[24]  G. Battese,et al.  A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies , 2004 .

[25]  T. Sueyoshi,et al.  Social sustainability measured by intermediate approach for DEA environmental assessment: Chinese regional planning for economic development and pollution prevention , 2017 .

[26]  Mohammad Reza Ghasemi,et al.  Carbon efficiency evaluation: An analytical framework using fuzzy DEA , 2016, Eur. J. Oper. Res..

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

[28]  Tao Ding,et al.  Centralized fixed cost and resource allocation considering technology heterogeneity: a DEA approach , 2018, Ann. Oper. Res..

[29]  Malin Song,et al.  Review of the network environmental efficiencies of listed petroleum enterprises in China , 2015 .

[30]  Lei Fang,et al.  Centralized resource allocation based on the cost-revenue analysis , 2015, Comput. Ind. Eng..

[31]  Joe Zhu,et al.  A bargaining game model for measuring performance of two-stage network structures , 2011, Eur. J. Oper. Res..

[32]  Kaveh Khalili Damghani,et al.  Uncertain network data envelopment analysis with undesirable outputs to evaluate the efficiency of electricity power production and distribution processes , 2015, Comput. Ind. Eng..

[33]  Michael G. Pollitt,et al.  The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants , 2010 .

[34]  Tjalling C. Koopmans,et al.  EFFICIENT ALLOCATION OF RESOURCES , 1949 .

[35]  G. Battese,et al.  Metafrontier frameworks for the study of firm-level efficiencies and technology ratios , 2008 .

[36]  B. W. Ang,et al.  Measuring environmental performance under different environmental DEA technologies , 2008 .

[37]  S. You,et al.  A new approach in modelling undesirable output in DEA model , 2011, J. Oper. Res. Soc..

[38]  Ali Emrouznejad,et al.  A bi-objective weighted model for improving the discrimination power in MCDEA , 2014, Eur. J. Oper. Res..