Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes
Carryover activities in dynamic DEA refer to the persistence of resources, inputs, or outputs across periods in organizational processes, reflecting the impact of past decisions on current and future performance. In practical applications, some carryover variables can extend beyond the immediate nex...
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| Language: | English |
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University of New Mexico
2025-04-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/9DynamicNetwork.pdf |
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| author | Maryam Heydar Hadi Bagherzadeh Valami |
| author_facet | Maryam Heydar Hadi Bagherzadeh Valami |
| author_sort | Maryam Heydar |
| collection | DOAJ |
| description | Carryover activities in dynamic DEA refer to the persistence of resources, inputs, or outputs across periods in organizational processes, reflecting the impact of past decisions on current and future performance. In practical applications, some carryover variables can extend beyond the immediate next period, and their allocation is discretionary, controlled by the Decision-Maker (DM). This paper introduces a novel dynamic network DEA (DNDEA) model aimed at optimizing the allocation of these carryovers and identifying inefficiencies within a network system across multiple evaluation periods. Recognizing the uncertainties present in real-world data, we incorporate neutrosophic sets to effectively process uncertain information, which adds complexity to our analysis. To address this, we transform the Neutrosophic Dynamic Network Slack-Based Measure (NDNSBM) model into a two-stage framework. By leveraging the concept of Pareto efficiency, our model establishes boundaries for overall and period scores across varying levels of truth, indeterminacy, and falsity. The key contribution of this work is the introduction of discretionary carryover variables in DNDEA models, facilitating strategic allocation across future periods. Additionally, the integration of neutrosophic data provides a more realistic approach to dynamic decision-making contexts. We validate our methodology through a numerical example evaluating the performance of Iranian bank branches, demonstrating that our proposed model is more discriminative and offers deeper insights into resource allocation strategies compared to the DNSBM model. This comprehensive approach enhances understanding of resource management in dynamic environments, offering valuable implications for decision-makers in various sectors. |
| format | Article |
| id | doaj-art-9cc1a2fff4ab47d081ff16f6d260ca39 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | University of New Mexico |
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| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-9cc1a2fff4ab47d081ff16f6d260ca392025-08-20T04:03:26ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-04-017913616810.5281/zenodo.14525384Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational ProcessesMaryam Heydar Hadi Bagherzadeh ValamiCarryover activities in dynamic DEA refer to the persistence of resources, inputs, or outputs across periods in organizational processes, reflecting the impact of past decisions on current and future performance. In practical applications, some carryover variables can extend beyond the immediate next period, and their allocation is discretionary, controlled by the Decision-Maker (DM). This paper introduces a novel dynamic network DEA (DNDEA) model aimed at optimizing the allocation of these carryovers and identifying inefficiencies within a network system across multiple evaluation periods. Recognizing the uncertainties present in real-world data, we incorporate neutrosophic sets to effectively process uncertain information, which adds complexity to our analysis. To address this, we transform the Neutrosophic Dynamic Network Slack-Based Measure (NDNSBM) model into a two-stage framework. By leveraging the concept of Pareto efficiency, our model establishes boundaries for overall and period scores across varying levels of truth, indeterminacy, and falsity. The key contribution of this work is the introduction of discretionary carryover variables in DNDEA models, facilitating strategic allocation across future periods. Additionally, the integration of neutrosophic data provides a more realistic approach to dynamic decision-making contexts. We validate our methodology through a numerical example evaluating the performance of Iranian bank branches, demonstrating that our proposed model is more discriminative and offers deeper insights into resource allocation strategies compared to the DNSBM model. This comprehensive approach enhances understanding of resource management in dynamic environments, offering valuable implications for decision-makers in various sectors.https://fs.unm.edu/NSS/9DynamicNetwork.pdfneutrosophic setdynamic network deadecision makingcarryover variables |
| spellingShingle | Maryam Heydar Hadi Bagherzadeh Valami Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes Neutrosophic Sets and Systems neutrosophic set dynamic network dea decision making carryover variables |
| title | Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes |
| title_full | Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes |
| title_fullStr | Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes |
| title_full_unstemmed | Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes |
| title_short | Neutrosophic Dynamic Network DEA: Efficient Allocation of Carryover Variables in Organizational Processes |
| title_sort | neutrosophic dynamic network dea efficient allocation of carryover variables in organizational processes |
| topic | neutrosophic set dynamic network dea decision making carryover variables |
| url | https://fs.unm.edu/NSS/9DynamicNetwork.pdf |
| work_keys_str_mv | AT maryamheydar neutrosophicdynamicnetworkdeaefficientallocationofcarryovervariablesinorganizationalprocesses AT hadibagherzadehvalami neutrosophicdynamicnetworkdeaefficientallocationofcarryovervariablesinorganizationalprocesses |