Identifying the Influential Factors in Increasing the Efficiency of Network Systems: A Mixed Binary Linear Programming
Old models in data envelopment analysis (DEA) consider decision-making units (DMUs) as black boxes. Therefore, they do not have a proper efficiency to evaluate network systems. This shortcoming has led to the emergence of network models that take the performance of a system’s processes into account...
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| Veröffentlicht in: | Operations Research Forum Jg. 4; H. 4; S. 68 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Cham
Springer International Publishing
01.12.2023
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 2662-2556, 2662-2556 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Old models in data envelopment analysis (DEA) consider decision-making units (DMUs) as black boxes. Therefore, they do not have a proper efficiency to evaluate network systems. This shortcoming has led to the emergence of network models that take the performance of a system’s processes into account in calculating the performance, and some of which also assign a certain value of performance to the processes. However, no model has examined the effect of intermediate factors in a network system, while the study of these intermediate factors can greatly help to increase the efficiency of a system. In this paper, our aim is to present a mixed binary linear programming that identifies the intermediate factors that are relatively more effective in increasing the performance of a network system. At the end, the new model is implemented on a small network system in order to better describe the performance, as well as on a real-world network system. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2662-2556 2662-2556 |
| DOI: | 10.1007/s43069-023-00259-8 |