An Equitable Fuzzy Approach for Facility Delocation: A Case Study of Banks Merging
Bibliographic record
Abstract
This paper aims to provide an equitable approach for the delocation via merging different bank branches. Due to the profit loss, some banks have resisted this change, so we developed a n/equity approach to modeling this issue to convince bank owners and employees. The proposed model is a mixed-integer programming model to have an equitable approach to fuzzy constraints based on the weighted sum of the remaining branches to the total number of branches of each type of bank. Moreover, this equitable approach was also used to avoid unemployment of the closed branches staff. Considering the harsh employment conditions and the turmoil of employees after the branched delocation, maximizing the retention of closed branch employees is considered the model's objective function. The result showed that using fuzzy constraints, equity can be well modeled. Moreover, increasing the equity coefficient reduces the number of facilities remaining in the system, and as a consequence, the desired efficiency (i.e., personnel retention) is reduced. So, we can reach the non-dominated answers. Finally, the results showed that reducing the minimum distance between facilities will allow more facilities to remain in the system and retain more staff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".