Allocation efficiency in network DEA

Authors

  • G. Tohidi Department of Mathematics, Islamic Azad University, Central Tehran Branch, Tehran-Iran
  • S. Banihashemi Department of mathematics, Computer and Statistics, Allame Tabataba’i University, Tehran, Iran
Abstract:

  The present study is an attempt towards remodeling cost, revenue and profit relationship within the network process. The previous models of Data Envelopment Analysis (DEA) have been too general in their scope and focused on the input and the output within a black box system, therefore they have not been able to measure various phases simultaneously within a network system. By using these models internal linking activities are neglected. A slacks-based network DEA model is dealt with intermediate products (Tone,Tsutsui). In this development, each input and output can use situations where unit price and unit cost information are available. In this paper we introduce models of cost, revenue and profit efficiency in network DEA. These models are illustrated by numerical examples and finally results of Constant Returns to Scale (CRS) are obtained. The findings could be used in minimizing the costs and maximizing the benefits in various organizations, public services, factories, and public and private sector companies.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

allocation efficiency in network dea

the present study is an attempt towards remodeling cost, revenue and profit relationship within the network process. the previous models of data envelopment analysis (dea) have been too general in their scope and focused on the input and the output within a black box system, therefore they have not been able to measure various phases simultaneously within a network system. by using these models...

full text

assessment of the efficiency of s.p.g.c refineries using network dea

data envelopment analysis (dea) is a powerful tool for measuring relative efficiency of organizational units referred to as decision making units (dmus). in most cases dmus have network structures with internal linking activities. traditional dea models, however, consider dmus as black boxes with no regard to their linking activities and therefore do not provide decision makers with the reasons...

Network DEA: Additive efficiency decomposition

Article history: Received 1 May 2009 Accepted 5 May 2010 Available online 9 May 2010

full text

Cost efficiency in three stage network DEA-R processes

In many organizations and financial institutions, we don't always have acsses to inputs and outputs to evaluate the decision-making units (DMUs), but rather only a ratio of inputs to outputs ( or reverse) might be available. In DEA, cost efficiency determines input standards based on input costs. In multi-stage network DEA processes, in addition to input standards, cost efficiency would determi...

full text

The overall efficiency and projection point in network DEA

Data Envelopment Analysis (DEA) is one of the best methods for measuring the efficiency and productivity of Decision Making Units (DMU). Evaluating the efficiency of DMUs which have two or several stages by using the conventional DEA models, is equal to consider them as black box. This method, omits the effect of intermediate measure on efficiency. Therefore, just the first network inputs and t...

full text

Fixed cost and resource allocation based on DEA cross-efficiency

In many managerial applications, situations frequently occur when a fixed cost is used in constructing the common platform of an organization, and needs to be shared by all related entities, or decision making units (DMUs). It is of vital importance to allocate such a cost across DMUs where there is competition for resources. Data envelopment analysis (DEA) has been successfully used in cost an...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 1  issue 2

pages  85- 96

publication date 2013-04-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023