Heuristics for the dynamic facility location problem with modular capacities
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Bibliographic record
Abstract
Abstract This paper studies the Dynamic Facility Location Problem with Modular Capacities (DFLPM). It generalizes several facility location problems and consists in determining locations and sizes of facilities to minimize location and demand allocation costs with decisions taken periodically over a planning horizon. The DFLPM is solved using heuristics tailored for different scenarios and cost structures. We propose three linear relaxation based heuristics (LRH) and an evolutionary heuristic that hybridizes a genetic algorithm with a variable neighborhood descent (GA+VND). We adapt benchmark instances from the literature to yield several representations of scenarios and parameters structures. Experiments are reported comparing the heuristics to a state-of-the-art mixed integer programming (MIP) formulation for the problem. We show that the performance of the methods depends on the characteristics of the instance solved. For the benchmark instances, the LRH improved by VND finds solutions within 0.02% of the optimal ones in less than half of the time of the MIP. For the scenarios where construction costs are higher and module sizes are lower, the GA+VND proved to be effective to solve the problem, outperforming the LRH and the MIP. We also discuss the results from a practitioner point of view to identify situations where each method is preferable.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it