Resources allocation in emergency response using an interdependencies simulation environment
Bibliographic record
Abstract
Natural and man-made disasters cause tremendous losses every year. Recent events, such as Hurricane Katrina and Sandy, have revealed the need for coordinated and effective emergency responses. In order to reduce human lives and economic losses, available resources should be allocated efficiently. Emergency responders are increasingly being challenged by the size and complexity of critical infrastructures that provide vital resources for emergency response operations. In this paper, we propose an integrated simulation-optimization tool for assisting emergency responders in finding the optimal allocation of available resources during a disaster event. The proposed tool utilizes the Infrastructure Interdependencies Simulator (i2Sim) for modeling the critical infrastructures that provides the available resources such as power and water. An optimization agent is developed based on a Genetic Algorithm (GA) to interact with the i2Sim simulator. We use this integrated simulation-optimization tool 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. The problem formulation incorporates the physical interdependencies between critical infrastructures in emergency response operations. 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".