João Carlos Colmenero

Sick Building Syndrome (SBS) refers to symptoms that building occupants can experience due to poor indoor environmental quality, such as respiratory problems, headaches, and fatigue. These symptoms are often associated with factors such as inadequate ventilation, insufficient humidity and temperature control, and contaminants in the air. The complexity of SBS requires advanced decision-making approaches, especially in scenarios involving multiple criteria evaluation. This article proposes applying the fuzzy COPRAS (Complex Proportional Assessment) method as a robust tool for deepening knowledge about SBS in built environments to mitigate its effects. The fuzzy methodology makes it possible to deal effectively with uncertainties and subjectivity, characterizing the weights of design resources and sub-resources in linguistic terms generated from triangular fuzzy numbers. Five alternatives and six factors related to indoor air quality, promoting occupant well-being, humidity and temperature control, advanced ventilation systems, and the implementation of indoor green spaces were evaluated. The COPRAS fuzzy model made it possible to rank these alternatives according to their relative effectiveness in mitigating SBS. The results indicate that internal green spaces and chemical and materials management policy are priorities, highlighting the importance of careful planning in choosing the most appropriate solutions to improve the health and well-being of building occupants.

Decision-making is a complex task for people, professionally or personally, since the decision has several positive and negative consequences and cannot always be undone. Multicriteria decision-making models (MCDM) aim to reduce the complexity of decision-making by using mathematical and algorithmic procedures to guide the process. Given the various issues and complexities that current methods for weighting criteria present; this study aims to develop a new subjective multicriteria method for criteria weighting applied in Green Supplier Selection (GSS). To solve this GSS problem, the Least to Most Range (LMR) method was developed and applied. The LMR is characterized by presenting a new logic for weighting criteria using two distinct and complementary scales. This method has a series of advantages over some well-established methods (e.g. AHP, BWM) in the literature. To illustrate these advantages, a numerical application was developed in GSS containing a literature review to survey relevant criteria for the problem. The LMR has proven robust compared to other methods because it can treat many criteria simultaneously without presenting inconsistency, satisfactorily dealing with some of the problems most commonly found in weighting methods in the literature.

The concept of smart cities has gained relevance over the past few years. Public managers have been planning investments to turn their cities into smart cities. Maturity models can help managers to monitor the performance of urban indicators; however, these maturity models are not always capable of meeting their proposed goals. In this sense, this research aims to develop a maturity model that ranks the “smartness” of a city based on social and technological indicators. The Smart Cities Maturity Model (MMSC) variables were extracted from ISO’s 37153:2017, 37120:2018, 37107:2019. The MMSC is structured on a hybrid TOPSIS multicriteria decision-making method. In this paper, we modified TOPSIS and used it to generate a synthetic indicator, called smart index, that designates the level of maturity of a real city. For this change to be possible, we fixed some alternatives and changed the positive ideal and negative ideal solution. The methodology is proven to be very efficient in measuring the smart city maturity level, and it can be easily adapted for the upcoming ISOs.

Purpose: Effective educational methods are critical for successfully training future supply chain talent. The paper proposes a fuzzy multi-criteria decision-making model to evaluate and select the best educational method for tomorrow's supply chain leaders integrating skill development priorities in an uncertain environment.Design/methodology/approach: The Grounded theory scheme is used to identify SC leaders' skillsets criteria and educational method alternatives. Fuzzy step-wise weight assessment ratio analysis sets the priority and determines the weight of 17 criteria. Eight decision-makers evaluate 13 alternatives using fuzzy linguistic terms. Fuzzy technique for order preference by similarity to ideal solution ranks and shows the most effective educational method. Sensitivity analysis presents the applicability of this study.Findings: Its implementation in a university-industry collaboration case in Brazil, Mentored learning from industry experts is the best educational method. The skill development priorities are data analytics ability, end-to-end supply chain vision and problem-solving. Technical skills are the most important criteria that influence the selection of the optimal option and educational methods related to learning from others rank in the top teaching pool, including multidisciplinary cross-cultural training.Originality/value: This paper is among the first to evaluate educational methods with skill development priorities integration for supply chain students using fuzzy SWARA–fuzzy TOPSIS. It provides actionable insights: a decision-making procedure for educational method selection, a broad skills profile for supply chain professional success and educational methods that professors can bring to in classroom/virtual environment.

Prioritizing information technology (IT) projects for resource direction is a complex decision‐making process that involves the analysis of qualitative and subjective criteria. The uncertainty of the subjective and imprecise assessments derived from estimated data that represent the analyzed criteria characterizes the decision‐making process. To reduce the uncertainties related to IT project priority setting, we proposed a model based on a hybrid multicriteria method composed by the best–worst method to establish the weights of the criteria and fuzzy‐TOPSIS to define the project ranking. The model allows us (1) to define strategic subcriteria, (2) adjust the weights according to the company's reality, (3) to deal with the uncertainties of the evaluations of the decision makers, and (4) provide the project ranking that translates reality into the distribution of resources in the IT sector. Finally, we applied the model to a case study in the IT sector of a Brazilian agroindustrial cooperative. For the managers, the method provides a precise decision aiding tool. Although the model has been applied to the prioritization of IT projects in a cooperative, the same can be generalized to the IT sectors of other companies.

Project evaluation is a process that involves multiple decision criteria for different objectives and involves several specialists, becoming a complex decision-making process. In this sense, to reduce projects evaluation subjectivity in start-ups selection processes, this paper aims at structuring an evaluation model to select projects that present business models focused on strategies for smart cities. To test the proposed model, it was considered a smart cities project selection program in a medium-size city located on southside of Brazil. To do so, it was considered a multicriteria analysis method, in which relevant evaluation criteria for projects focused on intelligent cities were identified. Subsequently, Decision-Making And Trial Evaluation Laboratories (DEMATEL) method was used to weigh these identified evaluation criteria and, finally, TOPSIS method was instantiated to select the best alternative – the evaluated projects – among all alternatives. As a result, the multicriteria proposed model proved to be efficient in evaluating projects aimed at smart cities concepts, showing that the use of MCDA methods can minimise uncertainty and bias in evaluating projects in a quantitative approach.

Uma das principais etapas de um projeto de cadeira de rodas é a definição de sua estrutura, que deve ser resistente para possibilitar a sua utilização em vários ambientes, leve para evitar a fadiga e possíveis problemas de saúde nos membros superiores do usuário, e possuir um custo adequado. O objetivo deste artigo é propor um método para a seleção do material da estrutura da cadeira de rodas de modo a auxiliar na etapa de desenvolvimento e melhoria de projetos de cadeiras de rodas. Para isso foi empregado o método multicritério de tomada de decisão Analytic Hierarchy Process (AHP), o qual considera critérios quantitativos e qualitativos predeterminados para classificar as alternativas analisadas. O método foi aplicado para a seleção do material da estrutura de uma cadeira de rodas considerando dez alternativas (aços, ligas de alumínio e titânio). Nessa análise, identificou-se que a liga de alumínio A7003-T5, é o melhor material para a estrutura de cadeiras de rodas.

The disordered urban growth that may favour the emergence of the Aedes aegypti mosquito in cities is a problem of increasing magnitude in middle- and high-income countries in the tropical part of the world. Currently, the World Health Organization (WHO) considers the control and elimination of Ae. aegypti a world-wide high priority as it is the main vector of many rapidly spreading viral diseases, dengue in particular. A major difficulty in controlling the proliferation of this vector is associated with identification of the breeding sites. The use of Unmanned Aerial Vehicles (UAVs) can be an efficient alternative to manual search because of high mobility and the ability to overcome physical obstacles, particularly in urban areas where it can offer close-up images of potential breeding sites that are difficult to reach. The objective of this study was to find a way to select the most suitable UAV for the identification of Ae. aegypti habitats by providing images of potential mosquito breeding sites. This can be accomplished by a Multiple-Criteria Decision Method (MCDM) based on an Analytical Hierarchy Process (AHP) for the evaluation of weights of the criteria used for characterizing UAVs. The alternatives were analyzed and ranked using the Fuzzy Set Theory (FST) merged with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The methodology is explained and discussed with respect to identification and selection of the most appropriate UAV for aerial mapping of Aedes breeding sites.