<![CDATA[The current “9-3” operational scheduling model used by the Ecuadorian National Police imposes rigid 8-hour rotational shifts over nine consecutive days, followed by three days off, without accounting for the spatiotemporal distribution of criminal activity. This leads to structural inefficiencies, including officer overload exceeding public-sector standards by 57%, unbalanced shift coverage, and an increase in fatigue-related incidents. This study aims to optimize staff allocation by proposing a data-driven redistribution model based on a normalized hour-day matrix. The method integrates multi-source institutional data, including ECU-911 dispatch logs, crime reports, and homicide records, and applies weighted normalization to construct proportional risk matrices per time slot. These matrices guide the redistribution of personnel while adhering to institutional criteria, including target monthly workload, equitable shift rotation, and guaranteed minimum coverage. The model was implemented in four pilot sectors characterized by varying urban, residential, and peripheral conditions. Results demonstrated improved adequacy in night-shift coverage of up to 30%, a 41% reduction in temporal imbalance, and decreased workload variability, with coefficients of variation below 6%. The proposed approach offers a replicable, low-cost planning solution that combines empirical risk modeling, operational transparency, and institutional scalability, representing a significant methodological improvement over the traditional static scheduling model.]]>
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