MINIMUM DATA SET REQUIRED FOR MULTIPLE CRITERIA DECISION MAKING MODELS IN OPERATIONAL SATURATION SCENARIOS FOR FIREFIGHTERS

Large-scale emergency management causes operational saturation in fire departments, where environmental demand exceeds cognitive processing capacity, leading to suboptimal decision-making. Although operational research proposes Dynamic Multi-Criteria Decision Making (DMCDM) models and Digital Twins to optimize resource allocation, their deployment fails if systems are fed with unstructured or non-standardized information. This study aims to scientifically define and validate a structured Minimum Data Set (MDS) to transition toward a proactive paradigm in emergency modeling. The Design Science Research (DSR) methodology was applied, aligned with the ISO/IEC 25010 quality standard. Based on a systematic literature review, an initial proposal of 63 operational variables was constructed. Validation was conducted using a two-round Modified Delphi method, consulting a panel of experts comprising operational emergency commanders from Andalusia. Linguistic evaluations were processed using Fuzzy Set Theory (Fuzzy Delphi), transforming them into Triangular Fuzzy Numbers and applying defuzzification algorithms to surpass mathematical thresholds of essentiality (>= 0.750) and consensus (>= 75%). Following the analytical process, a final MDS comprising 60 functional variables was validated. Results demonstrate that prevention and preparation phases require static variables suitable for establishing baselines using the Fuzzy AHP method. Conversely, the intervention phase inevitably requires the automation of kinetic data (IoT sensors and telemetry) coupled with TOPSIS algorithmic models for dynamic rerouting. It is concluded that the technological assimilation of this MDS reduces the commander's cognitive collapse and constitutes the essential semantic standard to integrate the tactical response of firefighters into Smart City Digital Twins.