<![CDATA[The cafe industry in Indonesia faces a business failure rate of 60-70% within the first three years, primarily caused by poor location selection, lacking systematic, data-driven methods (Lahindah et al., 2020; Nugraheni & Suparna, 2024). Existing studies tend to address location selection and route optimization independently. This separation leaves a gap in integrated multi-objective frameworks applicable to the Indonesian small-to-medium enterprise (SME) context. Unlike prior single-domain approaches, this study contributes a unified, replicable Decision Support System (DSS) that simultaneously integrates the Analytic Hierarchy Process (AHP), Pareto Multi-Objective Optimization, and the Traveling Salesman Problem (TSP) heuristic for optimal cafe location selection and logistics route efficiency. A sequential explanatory mixed-method design was applied, combining quantitative computational modeling with qualitative expert validation, across eight candidate locations in Tuban Regency, East Java, using spatial data from the Central Bureau of Statistics, OpenStreetMap, and Google Places API. Seven weighted criteria were evaluated via AHP (consistency ratio < 0.1), Pareto frontier analysis was conducted to identify non-dominated solutions, and three TSP algorithms (Nearest Neighbor, 2-Opt, Simulated Annealing) were implemented and compared. Results indicate that location L01 (Jl. Basuki Rahmat) achieved the highest DSS score of 0.797, a ranking that remained stable across the full sensitivity range of DSS weightings scenarios tested. The Simulated Annealing algorithm produced the most efficient distribution route of 4.22 km, reducing travel distance by 5.2% compared to the Nearest Neighbor baseline. Empirical validation against real-world Tuban data, measured as the proportion of parameters matching field observations within a 10% deviation threshold, demonstrated an 87.5% agreementrate. The proposed framework provides a replicable, transparent, and data-driven decision tool for SME cafe investors and municipal spatial planners.]]>
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