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Strategic Route Planning for Disaster Relief in Palu, Indonesia: A MILP Model Incorporating GIS Data.

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Titel: Strategic Route Planning for Disaster Relief in Palu, Indonesia: A MILP Model Incorporating GIS Data.
Autoren: Rohman, Muhammad Syaifur1 (AUTHOR) syaifur@dsn.dinus.ac.id, Saraswati, Galuh Wilujeng1 (AUTHOR), Shidik, Guruh Fajar1 (AUTHOR), Andono, Pulung Nurtantio1 (AUTHOR), Pramunendar, Ricardus Anggi1 (AUTHOR), Alomoush, Ashraf2 (AUTHOR), Saputra, Filmada Ocky1 (AUTHOR), Ratmana, Danny Oka1 (AUTHOR)
Quelle: Ingénierie des Systèmes d'Information. Jul2025, Vol. 30 Issue 7, p1869-1879. 11p.
Schlagwörter: *EMERGENCY management, *RESOURCE allocation, DISASTER relief, MIXED integer linear programming, GEODATABASES
Geografische Kategorien: INDONESIA
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. [ABSTRACT FROM AUTHOR]
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Datenbank: Business Source Index
Beschreibung
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. [ABSTRACT FROM AUTHOR]
ISSN:16331311
DOI:10.18280/isi.300719