Metaheuristics for data envelopment analysis problems

In conventional data envelopment analysis (DEA) models, some inputs and outputs may lose their weights in the process of measuring the relative efficiency of DMUs and it leads to ignore correspondi...

[1]  Ali Wagdy Mohamed Solving stochastic programming problems using new approach to Differential Evolution algorithm , 2017 .

[2]  Mehdi Toloo,et al.  A polynomial-time algorithm for finding epsilon in DEA models , 2004, Comput. Oper. Res..

[3]  Shiji Song,et al.  hybrid differential evolution algorithm for job shop scheduling problems with xpected total tardiness criterion , 2013 .

[4]  Ali Emrouznejad,et al.  A combined neural network and DEA for measuring efficiency of large scale datasets , 2009, Comput. Ind. Eng..

[5]  Gholam R. Amin,et al.  An Assurance Interval for the Non-Archimedean Epsilon in DEA Models , 2000, Oper. Res..

[6]  Alfredo Milani,et al.  Variable neighborhood algebraic Differential Evolution: An application to the Linear Ordering Problem with Cumulative Costs , 2020, Inf. Sci..

[7]  Mohammad Reza Alirezaee,et al.  The overall assurance interval for the non-Archimedean Epsilon in DEA models; a partition base algorithm , 2005, Appl. Math. Comput..

[8]  S. A. MirHassani,et al.  An efficient approach for computing non-Archimedean epsilon in DEA based on integrated models , 2005, Appl. Math. Comput..

[9]  L. Seiford,et al.  Computational Accuracy and Infinitesimals In Data Envelopment Analysis , 1993 .

[10]  Adel Hatami-Marbini,et al.  An ideal-seeking fuzzy data envelopment analysis framework , 2010, Appl. Soft Comput..

[11]  Emrah Hancer,et al.  A new multi-objective differential evolution approach for simultaneous clustering and feature selection , 2020, Eng. Appl. Artif. Intell..

[12]  Ali Emrouznejad,et al.  A novel inverse DEA model with application to allocate the CO2 emissions quota to different regions in Chinese manufacturing industries , 2018, J. Oper. Res. Soc..

[13]  Mohamed M. Mostafa,et al.  A probabilistic neural network approach for modelling and classifying efficiency of GCC banks , 2009 .

[14]  Kweku-Muata Osei-Bryson,et al.  Using Data Envelopment Analysis (DEA) for monitoring efficiency-based performance of productivity-driven organizations: Design and implementation of a decision support system , 2013 .

[15]  Reza Tavakkoli-Moghaddam,et al.  Addressing a nonlinear fixed-charge transportation problem using a spanning tree-based genetic algorithm , 2010, Comput. Ind. Eng..

[16]  Andreas C. Nearchou,et al.  Balancing large assembly lines by a new heuristic based on differential evolution method , 2007 .

[17]  Andreas C. Nearchou,et al.  Multi-objective balancing of assembly lines by population heuristics , 2008 .

[18]  Antonin Ponsich,et al.  Differential Evolution performances for the solution of mixed-integer constrained process engineering problems , 2011, Appl. Soft Comput..

[19]  Kankana Mukherjee,et al.  Measuring energy efficiency in the context of an emerging economy: The case of indian manufacturing , 2010, Eur. J. Oper. Res..

[20]  Ali Wagdy Mohamed,et al.  Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation , 2017, Soft Computing.

[21]  S. Molla‐Alizadeh‐Zavardehi,et al.  Genetic and differential evolution algorithms for the allocation of customers to potential distribution centers in a fuzzy environment , 2014 .

[22]  Ali Wagdy Mohamed,et al.  Constrained optimization based on modified differential evolution algorithm , 2012, Inf. Sci..

[23]  Gholam R. Amin,et al.  Minimizing greenhouse gas emissions using inverse DEA with an application in oil and gas , 2019, Expert Syst. Appl..

[24]  M. R. Alirezaee,et al.  Recognizing the efficiency, weak efficiency and inefficiency of DMUs with an epsilon independent linear program , 2006, Appl. Math. Comput..

[25]  Desheng Dash Wu,et al.  Supplier selection: A hybrid model using DEA, decision tree and neural network , 2009, Expert Syst. Appl..

[26]  Russell G. Thompson,et al.  Polyhedral Assurance Regions with Linked Constraints , 1994 .

[27]  Kweku-Muata Osei-Bryson,et al.  Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks , 2010, Eur. J. Oper. Res..

[28]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[30]  Ali Wagdy Mohamed,et al.  Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems , 2017, Appl. Comput. Intell. Soft Comput..

[31]  V. Charles,et al.  Stochastic simulation based genetic algorithm for chance constrained data envelopment analysis problems , 2011 .

[32]  Carlos Pestana Barros,et al.  Hotel efficiency: A bootstrapped metafrontier approach , 2010 .

[33]  Ghasem Tohidi,et al.  Uncertain RUSSEL data envelopment analysis model: A case study in iranian banks , 2019, J. Intell. Fuzzy Syst..

[34]  Mehdi Toloo,et al.  Evaluation efficiency of large-scale data set with negative data: an artificial neural network approach , 2015, The Journal of Supercomputing.

[35]  Guillermo Iglesias,et al.  Measurement of productive efficiency with frontier methods: A case study for wind farms , 2010 .

[36]  Rangan Gupta,et al.  Efficiency in South African agriculture: a two-stage fuzzy approach , 2018, Benchmarking: An International Journal.

[37]  Ali Azadeh,et al.  Forecasting electrical consumption by integration of Neural Network, time series and ANOVA , 2007, Appl. Math. Comput..

[38]  Joseph C. Paradi,et al.  Identifying managerial groups in a large Canadian bank branch network with a DEA approach , 2012, Eur. J. Oper. Res..