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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)


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
Publication statusPublished - Jan 2019


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

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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