Dionicio Neira-Rodado

Reliable electricity supply is essential for healthcare institutions, particularly where grid instability can disrupt service continuity, compromise patient safety, and affect the operation of critical medical equipment. In this context, selecting an appropriate photovoltaic (PV) proposal is a complex decision problem that requires assessing technical, economic, environmental, and regulatory factors jointly. This study develops a hybrid multi-criteria decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support PV proposal selection in healthcare institutions. The framework was applied to four competing proposals for a hospital case study in Barranquilla, Colombia. After integrating FAHP and DEMATEL, the economic, technical, and environmental criteria received balanced interdependence-adjusted weights of 0.324, 0.337, and 0.338, respectively. At the same time, DEMATEL identified the technical dimension as the main net influencing dimension within the expert-elicited influence network. The final ranking placed Proposal 1 first, followed by Proposal 4, Proposal 2, and Proposal 3, with closeness coefficients of 0.530, 0.518, 0.498, and 0.492, respectively. Additional comparative analysis showed that omitting DEMATEL changed the winning alternative, whereas preserving the FAHP–DEMATEL weighting structure and replacing TOPSIS with MARCOS yielded the same ranking. Robustness analyses further showed that the ranking remained stable in most supplier-exclusion and leave-one-expert-out scenarios. In contrast, bootstrap-based probabilistic sensitivity analysis showed that Proposal 1 ranked first in 96.2% of the replications. These results support the practical usefulness of the proposed framework for decision-making in healthcare energy planning.

Emergency medical services (EMSs) are critical to reducing fatalities and improving patient outcomes in emergencies such as traffic accidents, where response time is a decisive factor. This study proposes a comprehensive and systematic approach to designing and optimizing EMS systems tailored for urban traffic accidents. By integrating Geographic Information Systems (GISs), hypercube queuing models, Economic Value Added (EVA) calculations, and multi-criteria decision-making (MCDM) techniques, we developed a model that balances service efficiency, financial sustainability, and equitable access to emergency care. The hypercube queuing model was applied to estimate key performance metrics, such as response time, coverage, and the GINI index for equity, under varying numbers of ambulances and demand scenarios. In addition, EVA was calculated for different configurations of leased and owned ambulances, offering a financial perspective to assess the viability of public–private partnerships (PPPs) in EMSs. Using the fuzzy Analytic Hierarchy Process (AHP) and CoCoSo (Combined Compromise Solution) methods, this study identified the optimal number of ambulances required to minimize response time, maximize coverage, and ensure financial sustainability. The proposed approach has been applied to a real case in Colombia. Furthermore, integrating leased ambulances offers a financially viable solution with positive EVA values that guarantee the long-term sustainability of the public–private partnership. This paper advances the literature by providing a practical framework for optimizing EMS systems, particularly in developing countries where financial constraints and resource limitations represent significant challenges. The proposed methodology improves service efficiency and economic sustainability and ensures equity in access to life-saving care.