A combined goal programming and inverse DEA method for target setting in mergers

Gholam R. Amin, Saeed Al-Muharrami, Mehdi Toloo

Research output: Contribution to journalArticle

Abstract

This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry.

Original languageEnglish
Pages (from-to)412-417
Number of pages6
JournalExpert Systems with Applications
Volume115
DOIs
Publication statusPublished - Jan 1 2019

Fingerprint

Data envelopment analysis
Decision making
Industry

Keywords

  • Banking industry
  • Data envelopment analysis
  • Goal programming
  • Inverse data envelopment analysis
  • Mergers

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

A combined goal programming and inverse DEA method for target setting in mergers. / Amin, Gholam R.; Al-Muharrami, Saeed; Toloo, Mehdi.

In: Expert Systems with Applications, Vol. 115, 01.01.2019, p. 412-417.

Research output: Contribution to journalArticle

@article{3c36fb7ecdf143ea83dfb4cd0e600ce8,
title = "A combined goal programming and inverse DEA method for target setting in mergers",
abstract = "This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry.",
keywords = "Banking industry, Data envelopment analysis, Goal programming, Inverse data envelopment analysis, Mergers",
author = "Amin, {Gholam R.} and Saeed Al-Muharrami and Mehdi Toloo",
year = "2019",
month = "1",
day = "1",
doi = "10.1016/j.eswa.2018.08.018",
language = "English",
volume = "115",
pages = "412--417",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - A combined goal programming and inverse DEA method for target setting in mergers

AU - Amin, Gholam R.

AU - Al-Muharrami, Saeed

AU - Toloo, Mehdi

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry.

AB - This paper suggests a novel method to deal with target setting in mergers using goal programming (GP) and inverse data envelopment analysis (InvDEA). A conventional DEA model obtains the relative efficiency of decision making units (DMUs) given multiple inputs and multiple outputs for each DMU. However, the InvDEA aims to identify the quantities of inputs and outputs when efficiency score is given as a target. This study provides an effective method that allows decision makers to incorporate their preference in target setting of a merger for saving specific input(s) or producing certain output(s) as much as possible. The proposed method is validated through an illustrative application in banking industry.

KW - Banking industry

KW - Data envelopment analysis

KW - Goal programming

KW - Inverse data envelopment analysis

KW - Mergers

UR - http://www.scopus.com/inward/record.url?scp=85053382000&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053382000&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2018.08.018

DO - 10.1016/j.eswa.2018.08.018

M3 - Article

VL - 115

SP - 412

EP - 417

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -