Open AccessA Linear Programming Based DEA-PROMETHEE Approach for Performance Evaluation
Author(s) -
Uğur Tahsin Şenel,
Babak Daneshvar Rouyendegh,
Adem Pınar
Publication year - 2021
Publication title -
maǧallaẗ al-abḥāṯ al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2307-1885
pISSN - 2307-1877
DOI - 10.36909/jer.13279
Subject(s) - data envelopment analysis , ranking (information retrieval) , multiple criteria decision analysis , linear programming , computer science , rank (graph theory) , operations research , preference , mathematical optimization , goal programming , mathematics , artificial intelligence , algorithm , statistics , combinatorics
Companies follow their objectives with some critical success factors (CSF), and they know their bottlenecks and strong points. This provides decision support for them, but this method ignores overall performance and ranking issues. In this study, a comprehensive methodology is recommended to find out an effective solution to the performance evaluation problem for making strategic performance management. Two methods are used from different areas as a framework. To select the higher-performing departments, Data Envelopment Analyses (DEA) is used as a linear programming-based main method. Moreover, a Multi-Criteria Decision Making (MCDM) method is proposed, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), to increase the discrimination power of DEA and eliminate undesirable results because of determining weight bounds. These two methods are combined, and a comprehensive solution model is presented in the study. In the end, a case study is given for a real-life example, an integrated DEA-PROMETHEE method is applied to the case. When the case results are examined, the proposed model produces more logical weight values and better results.