Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data

Supplier selection plays a key role in an organization because the cost of raw material constitutes the main cost of the final product. Selecting an appropriate supplier is now one of the most important decisions of the purchasing department. This decision generally depends on a number of different criteria. The objective of this paper is to propose a data envelopment analysis methodology that considers both undesirable outputs and imprecise data simultaneously. The proposed model is applied in supplier selection problem. A numerical example demonstrates the application of the proposed method.     

A new approach for prioritization in fuzzy AHP with an application for selecting the best tunnel ventilation system

Tunnel design and construction consist of multiple sub-processes among which the selection of proper ventilation system is one of the most important issues. In this problem, several criteria are included in the process; therefore, multi-attribute decision making (MADM) tools should be used for the purpose. One of frequently used MADM methods is fuzzy analytic hierarchy process (AHP) in which uncertainty is also taken into account in the decision-making process. In this paper, using fuzzy data envelopment analysis, a new approach is presented for ranking the alternatives in fuzzy AHP. In this approach, the fuzzy nature of data is kept in all steps of the approach. Furthermore, to determine the consistency level of fuzzy pairwise comparison matrix, an appropriate index is used. Then, the proposed approach is used for selecting the best tunnel ventilation system.     

Data envelopment analysis and robust optimization: A review

This paper reviews the milestone approaches for handling uncertainty in data envelopment analysis (DEA). This paper presents the detailed classifications of robust data envelopment analysis (RDEA). RDEA is appropriate for measuring the efficiencies of decision-making units in the presence of the data and distributional uncertainties. This paper reviews scenario-based and uncertainty set of DEA models. It covers 73 studies from 2008 to 2019. The paper concludes with suggestions about the guidelines for future researches in the field of RDEA.     

A new model for selecting third-party reverse logistics providers in the presence of multiple dual-role factors

In traditional models of data envelopment analysis it is assumed that decision making units do not have dual-role factors. This paper proposes a model for selecting third-party reverse logistics (3PL) provider in the presence of multiple dual-role factors. A numerical example demonstrates the application of the proposed method.     

A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors

Outsourcing in logistics is a very significant theme and third-party reverse logistics (3PL) provider evaluation and selection has to be realized in a careful manner in order to provide the expected benefits. In this paper a new chance-constrained data envelopment analysis (CCDEA) approach is proposed to assist the decision makers to determine the most appropriate third-party reverse logistics (3PL) providers in the presence of both dual-role factors and stochastic data. A numerical example demonstrates the application of the proposed model.     

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.     

A new data envelopment analysis (DEA) model to select eco-efficient technologies in the presence of undesirable outputs

Recent global attention to the challenges of environmental protection is forcing firms and governments to evaluate, rank, and select eco-efficient technologies. Technologies may consume inputs to produce both desirable and undesirable outputs. It seems that the data envelopment analysis (DEA) is a proper method to evaluate eco-efficient technologies. There are some DEA extensions for dealing with undesirable output, and sometimes it is difficult to choose a suitable model to evaluate the technologies. The challenge becomes much more complex when the outcomes of models are not similar. In such a condition, subjective selection of alternative DEA models may lead to deviation from an optimal decision. Accordingly, the objective of this paper is to develop a combined model to include all characters of the previous DEA techniques in a flexible model to select optimum eco-efficient technology in the presence of undesirable outputs. A case study demonstrates the application of proposed procedure.     

Developing a novel inverse data envelopment analysis (DEA) model for evaluating after-sales units

This paper proposes a novel model of inverse data envelopment analysis (IDEA) based on the slack-based measure (SBM) approach. The developed inverse SBM model can maintain relative efficiency of decision making units (DMUs) with new input and output. This model can also measure the input and output volumes when a decision maker (DM) increases efficiency score. The inverse SBM model is a kind of multi-objective non-linear programming (MONLP) problem, which is not easy to solve. Therefore, we suggest a linear programming model for solving inverse SBM model. In this model efficiency score of DMU under evaluation remains unchanged. Furthermore, we suggest an optimal combination of inputs and outputs in the production possibility set (PPS). A case study is presented to demonstrate the efficacy of our proposed model.     

How to assess sustainability of countries via inverse data envelopment analysis?

In this paper, two input-oriented and output-oriented inverse semi-oriented radial measures are presented. Such models are applied to determine resource allocation and investment strategies for assessing sustainability of countries. Our proposed models can deal with both positive and negative data. In our proposed inverse input-oriented data envelopment analysis (DEA) model, optimal inputs are suggested while outputs and efficiency score of decision-making unit (DMU) under evaluation are unchanged. Similarly, in our proposed inverse output-oriented DEA model, optimal outputs are proposed while inputs and efficiency score of DMU under evaluation are kept unchanged. For the first time, we propose two new inverse DEA models to handle resource allocation and investment analysis problems given sustainable development aspects in the presence of negative data. A case study is given for assessing sustainability of countries.     

Measuring congestion in sustainable supply chain based on data envelopment analysis

Neural Computing and Applications - Sustainable Supply Chain Management (SSCM) involves the integrating of environmental, social and economic concerns into supply chain management activities with...