The problem of infeasibility arises in conventional radial super-efficiency data envelopment analysis (DEA) models under variable returns to scale (VRS). To tackle this issue, a Nerlove–Luenberger (N–L) measure of super-efficiency is developed based on a directional distance function....

The conventional data envelopment analysis (DEA) assumes that the inputs and outputs are real values. However, in many real world instances, some inputs and outputs must be in integer values. While integer-valued DEA models have been proposed, the current paper develops an integer-valued DEA...

Conventional data envelopment analysis (DEA) methods assume that input and output variables are continuous. However, in many real managerial cases, some inputs and/or outputs can only take integer values. Simply rounding the performance targets to the nearest integers can lead to misleading...

Production in large organizations with a centralized decision-making environment like supermarket chains or factories with many workshops, usually involves the participation of more than one individual unit, each contributing a part of the total production. This study is motivated by a...

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs), where the internal structures of DMUs are treated as a black-box. Recently DEA has been extended to examine the efficiency of DMUs that have two-stage network structures or processes,...

In many settings, systems are composed of a group of independent sub-units. Each sub-unit produces the same set of outputs by consuming the same set of inputs. Conventional data envelopment analysis (DEA) views such a system as a "black-box", and uses the sum of the respective inputs and outputs...

The paper studies the result of Zhu21 and establishes a relationship between the efficiency in data envelopment analysis (DEA) and the pareto optimality under multiple objective linear programming (MOLP). It is shown that the DEA/preference structure models in Zhu21 can be derived by traditional...

Data envelopment analysis (DEA) identifies an empirical efficient frontier of a set of peer decision making units (DMUs) with multiple inputs and outputs. The efficient frontier is characterized by the DMUs with an unity efficiency score. The performance of inefficient DMUs is characterized with...