Search

Abstract This study describes a dataset containing urban fire events that took place in mainland Portugal between 2013 and 2022. The Regulation n.º3317-A/2018, established by the Portuguese National Emergency and Civil Protection Authority (Autoridade Nacional de Emergência e Proteção Civil, ANEPC), defines the Operations Management System (Sistema de Gestão de Operações, SGO). Among other attributions, this system allows to manage the lyfe-cycle of the urban fire events, from ignition to extinction, through the Operations Decision Support System (Sistema de Apoio à Decisão Operacional, SADO). This system supports the systematic collection of a minimum set of data on each event. All instances included in the dataset were retrieved from SADO. To make the data suitable for analytic purposes, several pre-processing actions were taken, including the steps of data transformation and cleaning. The dataset was further validated by a set of technical procedures aiming to verify both data correctness and utility. The final dataset provides the most recent multi-year record of Portuguese urban fires including 27 variables on 72641 events.

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.

Additive manufacturing (AM) is an emerging area with the potential to modify present business models in the near future. In contrast with conventional manufacturing (CM), AM allows the development of a product from a smaller amount of raw material, while allowing an improvement in properties in terms of weight and functionality. Its production flexibility and creativity in terms of materials have enabled not only the industry to use this technology, but also has been used in healthcare (e.g., in the production of human tissue) and by the final consumer. Despite the invaluable opportunities that this technology could provide, the uncertainties concerning its future developments and impacts on business models remain. New business models in AM will convey the need to: specialize the workforce in the design of new parts produced locally or remotely; regulation in the use and sharing of intellectual property rights by partner companies or between users; regulate the possibility of reverse engineering of highly customized products; etc. The present research proposes a conceptual maturity model to support the phases of evolution of AM in the industry, in supply chains, and in terms of open business models.

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.

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.