Flexible Districting Policy for the Multiperiod Emergency Resource Allocation Problem With Demand Priority
Bibliographic record
Abstract
To address responsiveness, time-dependence, and limited emergency supply issues, we introduce a new flexible districting policy, aiming to improve satisfaction in multiperiod emergency resource allocation (MPERA), and set demand priorities to guarantee allocation balance in resource-limited scenarios. The modeling and solution process involves the following: 1) formulating a mixed-integer programming (MILP) model for MPERA with demand priority (MPERA-DP), aiming to maximize utility considering the transportation cost, districting change, and penalty for unsatisfied demand and 2) incorporating the justifiable granularity principle (JGP) and particle swarm optimization (PSO) into the brand-and-price (B&P) algorithm for initial districting and allocating decisions to improve the solution quality and calculation speed. The results of the experiments show that 1) the JGP-PSO-B&P algorithm achieves superior efficiency in terms of optimality and convergence for large-scale cases. This algorithm could improve the optimality by 13.42% compared with that of the JGP-PSO algorithm, 13.15% compared with that of the B&P algorithm, and 28.18% compared with that of the PSO algorithm, on average; 2) the MPERA-DP model with flexible districting policy outperforms flexible MPERA without demand priority, emergency resource allocation with rescheduling (ERAR) and fixed emergency resource allocation with demand priority (FERA-DP), improving the utility by 20.56%, 5.14% and 41.84%, respectively; and 3) the scheme efficiency is influenced by the desirable satisfaction deviation, and when set to 0.6, it allows for the optimization of both demand satisfaction and utility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".