Resources allocation in disaster response using Ordinal Optimization based approach
Bibliographic record
Abstract
Recent events, such as Hurricane Katrina, have revealed the need for coordinated and effective disaster responses. An optimal distribution of available resources is essential for disaster response effectiveness. Emergency responders are faced with the challenges of increased size and complexity of critical infrastructures that provide vital resources for disaster response operations. In this paper, we propose a simulation-based tool to assist emergency responders in finding the optimal distribution of available resources during a disaster event. The proposed tool utilizes the Disaster Response Network Enabled Platform (DR-NEP) which is an infrastructure interdependencies simulation platform for disaster response support. DR-NEP is a simulation network platform that integrates different simulators for different infrastructures to form a universal simulation platform. We employ a new concept in Discrete Event Systems optimization called Ordinal Optimization to address the problem of resources allocation during a disaster event. The objective of the optimization problem is maximizing the operational capacity of a critical infrastructure, a hospital in this case. Due to the huge combinatorial feasible search space, an Ordinal Optimization based approach is used to solve the problem using two main concepts: goal softening and order comparison. This approach aims at finding a Good Enough solution set (G) with an acceptable probability and efficient computational effort. This paper describes early results of our work that shows the use of our approach in optimizing resources allocation in a simulated disaster event.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".