Strategic Route Planning for Disaster Relief in Palu, Indonesia: A MILP Model Incorporating GIS Data
Bibliographic record
Abstract
The 2018 earthquake and tsunami in Palu, Indonesia, highlighted critical inefficiencies in disaster relief distribution, including suboptimal resource allocation and delivery delays that significantly impact survival rates.This research develops a Mixed-Integer Linear Programming (MILP) model integrated with Geographic Information System (GIS) data to optimize post-disaster logistics distribution in Palu, Indonesia, aiming to minimize total distance and delivery time while ensuring adequate distribution to relief posts.The methodology incorporates geospatial data from 22 relief posts and 2 main warehouses using the PuLP library in Python.Data preprocessing included coordinate conversion, GeoDataFrame creation, and logistics demand calculation based on refugee populations.The optimization model minimizes the objective function Z = Σᵢ∈ₚΣⱼ∈ₚ dᵢⱼxᵢⱼ subject to demand satisfaction and vehicle capacity constraints.Results demonstrate significant operational efficiency improvements, achieving a 30% reduction in average delivery time and identifying 366 optimized distribution routes ranging from 0.87 km to 15.9 km.The model successfully allocated various logistics types including food, water, clothing, and medical supplies while respecting 20,000 kg vehicle capacity constraints.The integration of MILP with GIS data proves effective for disaster relief logistics optimization, enabling precise decision-making in emergency situations.This framework reduces failure risks in disaster response and improves recovery outcomes for affected communities, with implications for enhanced disaster management strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".