A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks

A production network can be described as a collection of production processes performed by several interdependent groups of sub decision-making units (SDMUs) within a DMU. Dynamic effects pertain to the situation where intermediate outputs consumed by one SDMU may also dynamically influence its output level in the future. Without considering these effects in efficiency measurement, we would obtain biased efficiency measurement, because the measure could not faithfully reflect the underlying performance. Hence the result would provide misleading information to decision-makers. This paper proposes a network-DEA model with new efficiency measures to systematically cope with the dynamic effect within a production network. Various interconnections between the new measure and the DEA-efficiency have also been established. Additionally, we also formalize the relationship between returns-to-scale properties of DMUs and those of its constituting SDMUs. This paper presents a unified framework to analyze performances in a dynamic production network.     

Stability of the classification of returns to scale in FDH models

This paper deals with the estimation of returns to scale (RTS) in free disposal hull (FDH) models and provides some stability intervals for preserving the RTS classification. It has been shown that the proposed stability intervals can be obtained via a polynomial-time algorithm based on the calculation of certain ratios of inputs and outputs, without solving any mathematical programming problem. The results of the study have been proved via some lemmas and theorems and have been illustrated by numerical examples and a real application.