Network data envelopment analysis with fuzzy non-discretionary factors

Non-discretionary Factors in Data Envelopment Analysis: Review and Extension

A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...

Data Envelopment Analysis Models In The Presence Of Ratio Data and Non-Discretionary Factors

A method to consider Non-Discretionary factors in Data Envelopment Analysis

The technique for efficiency measurement known as Data Envelopment Analysis (DEA) has been extended to allow on-discretionary inputs that affect production. Several methods exist for measuring efficiency to control these factors in production. This paper review these approaches, providing a discussion of strengths and weaknesses and highlighting potential limitations. In addition, a new approac...

Supplier selection using chance- constrained data envelopment analysis with non-discretionary factors and stochastic data

The changing economic conditions have challenged many organizations to search for more efficient and effective ways to manage their supply chain. During recent years supplier selection decisions have received considerable attention in the supply chain management (SCM) literature. There are four major decisions that are related to the supplier selection process: what product or services to order...