<![CDATA[Effective disaster logistics management requires rapid, data-driven decision support that bridges optimization theory and operational practice. Existing systems either rely on theoretical models without implementable software, on proprietary datasets that restrict independent reconstruction, or lack validated prototypes in the Indonesian disaster context — three gaps that persist across the disaster IS literature. This study presents a three-tier web-based disaster logistics management IS integrating GIS and MILP optimization, built exclusively on public data sources (BNPB DIBI and OpenStreetMap). Using Design Science Research (DSR) across five phases, the system employs an open-source stack: Laravel 11.x presentation layer, PostgreSQL 16/PostGIS data layer, and Python FastAPI as a dedicated MILP microservice. The MILP model, a two-phase lexicographic MILP formulation with trips-aware vehicle capacity constraints is solved using the PuLP 3.3.0 + CBC solver. Three integrated modules were developed: shelter management, warehouse inventory, and logistics coordination with GIS visualization. Functional testing achieved 100% pass rate across 85 automated test cases covering all system modules, with 246ms mean response time under 50 concurrent users. The MILP solver resolved a 20-shelter problem in 0.094 seconds (99.9% below the 120-second operational planning threshold); scalability testing confirms tractability from 10 to 50 shelters (0.011–0.111 seconds), with Priority-1 shelters consistently served under both sufficient and scarce fleet conditions. Sensitivity analysis confirms lexicographic priority objectives activate correctly under resource scarcity. Comparative evaluation against heuristic and metaheuristic approaches confirms exact MILP is appropriate for the strategic planning scope of this proof-of-concept (n ≤ 50 shelters). Expert validation via ISO 25010 yielded a weighted score of 4.21/5. Usability testing with 25 participants produced a SUS score of 74.8 (Grade B, above-average per established SUS benchmarks) with 88% task completion rate. The primary contributions are a MILP-IS microservices integration pattern with explicit API specification, a comprehensively documented public-data-only implementation framework, and a proof-of-concept that closes the implementation gap between disaster logistics optimization research and operational IS deployment.]]>
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