Bibliographic record
Abstract
The dynamic facility layout problem (DFLP) is to decide the locations of the departments in a facility over multiple planning periods. The main challenge in the DFLP is that there are two conflicting objectives of the problem: minimizing the material handling cost and the rearrangement cost. The cost of changing the structure of the layout over the planning horizon is also considered. Therefore, we model and solve the unequal area zone-based DFLP (ZDFLP) where the dimensions of the departments are decision variables and the departments are assigned to flexible zones with a pre-structured positioning. A zone-based block layout inherently includes possible aisle structures which can easily be adapted to the material handling system in use. This is particularly important in the DFLP because the changes in a block layout may require structural modifications in the material handling system, which in turn may be very costly. The proposed approach also considers determining relative department locations, their dimensions, as well as input/output (I/O) points concurrently for the first time in the literature. A new matheuristic, which combines concepts from simulated annealing, variable neighbourhood search, and mixed-integer programming, is used to solve the ZDFLP with promising results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".