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Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally.

Fire departments’ performance assessment (FDPA) is an important task for fire protection service (FPS) authorities to evaluate fire departments’ (FDs) efficiencies, identify the efficient FDs, and identify areas for improvement of the inefficient units. Therefore, many countries all around the world conducted FDPA research and published its results. Although Portugal is a country with many old cities and buildings, to the best of our knowledge, no FDPA study has been conducted. Hence, the objective of this paper is to conduct a FDPA on Portuguese (PT) FDs using the general FDPA framework and slack-based data envelopment analysis. The results of analyzing the 376 PT-FDs’ data in 2020 showed that only 22 out of 376 FDs were efficient; in addition, in most districts in Portugal, less than 10% of FDs were efficient, and a high percentage of FDs had less than 50% efficiency. Further details and findings are discussed, and some recommendations are provided throughout the paper. The paper’s findings could help the FPS decision maker have a better view of the FDs’ performances and take corrective actions to improve efficiencies. It can also help the FDPA evaluator to have a better understanding of how a FDPA can be conducted and discussed.

A significant amount of research has been conducted on the resource allocation in fire departments (RAFD) and literature reviews about the fire protection service (FPS), but to the best of our knowledge, no literature reviews have been conducted about the RAFD. Therefore, the purpose of this research is to review literature about allocating resources to urban fire departments (FDs) to gain state-of-the-art knowledge of RAFD and identify the most frequent methodologies and measures in the studies. A five-stage structured literature review (SLR) is undertaken to analyze the RAFD-related studies; subsequently, statistical analysis is used to disclose additional information from the retrieved data and develop a general framework for RAFD. According to the structured literature review, which yielded 417 independent variables for RAFD, integer programming (IP) and data envelopment analysis (DEA) are the most common approaches for RAFD among the mathematical and statistical models in the evaluated articles. Based on the findings, a general conceptual framework for RAFD is suggested. The findings of this study can help public and private FDs and FPS managers, decision-makers, resource allocation (RA) researchers, and academicians gain state-of-the-art knowledge of RAFD. The proposed RAFD framework can provide the FPS decision-makers with the appropriate method and variables for allocating their limited resources in a more efficient way within their FDs.