A Common Weight Multi-criteria Decision analysis-data Envelopment Analysis Approach with Assurance Region for Weight Derivation from Pairwise Comparison Matrices

Authors

Abstract:

Deriving weights from a pairwise comparison matrix (PCM) is a subject for which a wide range of methods have ever been presented. This paper proposes a common weight multi criteria decision analysis-data envelopment analysis (MCDA-DEA) approach with assurance region for weight derivation from a PCM. The proposed model has several merits over the competing approaches and removes the drawbacks of the well-known DEAHP and DEA/AR methods. Some numerical examples are provided from the literature in order to confirm the merits of the proposed method and its applications in multi criteria decision making. Results disclose the advantages of the proposed approach.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

A Common Weight Data Envelopment Analysis Approach for Material Selection

Material selection is one of the major problems in manufacturing environments since the improper selected material may lead to fail in the production processes and result in customer dissatisfaction and cost inefficiency. Every material has different properties which should be considered as major criteria during the material selection procedure. Selection criteria could be quantitative or quali...

full text

An Augmented Common Weight Data Envelopment Analysis for Material Selection in High-tech Industries

Material selection is a challenging issue in manufacturing processes while the inappropriate selected material may lead to fail the manufacturing process or end user experience especially in high-tech industries such as aircraft and shipping. Every material has different quantitative and qualitative criteria which should be considered simultaneously when assessing and selecting the right materi...

full text

Efficient weight vectors from pairwise comparison matrices

Pairwise comparison matrices are frequently applied in multi-criteria decision making. A weight vector is called efficient if no other weight vector is at least as good in approximating the elements of the pairwise comparison matrix, and strictly better in at least one position. A weight vector is weakly efficient if the pairwise ratios cannot be improved in all nondiagonal positions. We show t...

full text

A new approach for weight derivation using data envelopment analysis in the analytic hierarchy process

Recently, some researches have been carried out in the context of using data envelopment analysis (DEA) models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). One of these models is the DEAHP. The main drawback of the DEAHP is that it generates counter-intuitive priority vectors for inconsistent pairwise comparison matric...

full text

A Proposed Combination Method for Ranking Options in Multi-Criteria Decision Making by Data Envelopment Analysis and Common Set of Weights

The purpose of this paper is to fully ranking decision-making units using a combination of multi-criteria decision-making techniques and data envelopment analysis. Due to this fact that weights play an important role in ranking the options by multi-criteria decision-making methods and most of these methods have weakness in using weighting methods, therefore the ability for data envelopment anal...

full text

Consistent weight restrictions in data envelopment analysis

It has recently been shown that the incorporation of weight restrictions in models of data envelopment analysis (DEA) may induce free or unlimited production of output vectors in the underlying production technology, which is expressly disallowed by standard production assumptions. This effect may either result in an infeasible multiplier model with weight restrictions or remain undetected by n...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 28  issue 12

pages  1746- 1755

publication date 2015-12-01

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