Summary:
Liquefied Petroleum Gas (LPG) is a key clean cooking alternative to biomass, especially in developing countries where household air pollution remains a major concern. This study proposes a scalable decision-making framework for the design of LPG distribution networks, using Rwanda as a case study. We formulate a hierarchical location–allocation model as a Mixed-Integer Linear Program (MILP), leveraging a large-scale dataset with rooftop-level LPG demand for over 3.3 million households across Rwanda. To enable tractable, country-scale optimization, we adopt two complementary strategies: (i) a time-aggregated formulation assuming stable seasonal demand, and (ii) a spatial aggregation method based on agglomerative hierarchical clustering, which places retailers at distance-constrained geomedian points of rooftop clusters. We compare this clustering-based approach against a benchmark that uses village centroids for retailer siting, demonstrating cost savings and improved spatial fairness. Additionally, we assess the scalability of the system under projected demand growth and evaluate infrastructure–transportation trade-offs under fluctuating diesel prices. Our findings underscore the potential of data-driven planning tools in advancing equitable access to clean cooking solutions.
Spanish layman's summary:
Este estudio desarrolla un modelo de optimización para diseñar redes de distribución de GLP en Ruanda utilizando datos geoespaciales de 3,3 millones de hogares. El método de clustering espacial mejora la eficiencia en la ubicación de puntos de venta respecto a enfoques tradicionales, reduciendo los costes mientras promueve el acceso a cocinado limpio.
English layman's summary:
This study develops a scalable optimization framework for designing LPG distribution networks in Rwanda using rooftop-level data from 3.3 million households. A spatial clustering method improves retailer siting efficiency compared to traditional approaches, reducing costs while advancing equitable access to clean cooking fuels.
Keywords: Clean cooking; Mixed-integer linear programming; Location–allocation; Agglomerative clustering; Energy access planning; Supply chain optimization; Sustainable development goal 7
JCR-JIF Impact Factor and WoS quartile: 5,200 - Q2 (2025)
DOI reference:
https://doi.org/10.1016/j.esd.2026.101947
Published on paper: June 2026.
Published on-line: February 2026.
Citation:
Z.G. Gurkan, P. Dueñas, A.S. Kocaman, "Data-driven location–allocation for clean cooking LPG supply chains: A mixed-integer programming approach for Rwanda", Energy for Sustainable Development, Vol. 92, pp. 101947, June 2026. [Online: February 2026] doi: 10.1016/j.esd.2026.101947
