Sustainable and Integrated Decision Support System Model for Humanitarian Logistics in Indonesia

Floods remain one of Indonesia’s most recurrent and most disruptive disasters, yet humanitarian logistics operations often suffer from major challenges, such as fragmented coordination, unintegrated data, and delayed logistic needs distribution during emergency response. This study proposes a sustainable and integrated Decision Support System (DSS) algorithm based on a holistic approach, fuzzy logic, and a greedy algorithm to enhance effective logistics planning. The system was applied to eleven flood-prone sub-districts in Bogor Regency, using data from the National Agency for Disaster Countermeasure (BNPB) and the Indonesian Central Agency of Statistics (BPS). Based on the fuzzy inference system, this study determines priority levels under uncertainty of demographic vulnerability, hazard exposure, and access to health facilities, while the greedy algorithm optimizes logistics routing and warehouse allocation. The results showed that the system was able to schedule aid distribution across six days using three warehouses. All high-priority areas received assistance within the first two days, significantly improving delivery equity and response efficiency of the disaster response. The proposed model supports sustainable decision-making aligned with the Sendai Framework for Disaster Risk Reduction and the UN Sustainable Development Goals (SDG 11 and 13). This research contributes to the development of adaptive, data-driven humanitarian systems for disaster-prone regions