Alzira Mota

Abstract
This paper compares the performance of Simulated Annealing and Tabu Search meta-heuristics in addressing a parallel machine scheduling problem aimed at minimizing weighted earliness, tardiness, total flowtime, and machine deterioration costs-a multi-objective optimization problem. The problem is transformed into a single-objective problem using weighting and weighting relative distance methods. Four scenarios, varying in the number of jobs and machines, are created to evaluate these metaheuristics. Computational experiments indicate that Simulated Annealing consistently yields superior solutions compared to Tabu Search in scenarios with lower dimensions despite longer run times. Conversely, Tabu Search performs better in higher-dimensional scenarios. Furthermore, it is observed that solutions generated by different weighting methods exhibit similar performance. © 2025 The Author(s).

Abstract
Airborne Wind Energy (AWE) systems represent an innovative method for capturing wind energy at high altitudes, where wind conditions are typically stronger and more consistent. These systems utilize flying devices tethered to a ground station to harness wind energy. An AWE system comprises a tether connecting the flying device to a base station, a control system for maneuvering the device, and a mechanism for converting kinetic energy into electricity. Researchers are exploring various materials, designs, and control methods to enhance the efficiency and reliability of AWE systems. Over the past decade, interest in AWE has surged, leading to a substantial increase in scholarly publications on the topic. This research conducts an in-depth bibliometric analysis. This analysis highlights emerging topics, allowing researchers to identify new trends and areas of interest within a field. By emphasizing these emerging topics, researchers and stakeholders can better align their efforts with the latest developments and opportunities in their area of study. Findings reveal that research on control techniques in AWE has grown at an average annual rate of 16% since 2013. Additionally, the study identifies the most influential aspects of the literature, including key topics, articles, authors, and keywords. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Water is at the core of sustainable development, and its use for human activities, including vehicle washing, should be done in a sustainable way. There are several technical solutions for washing buses offering different performances, making it difficult to choose the one that best meets the requirements of each specific case. The literature on the topic hardly analyzes the choice of the best technical solution for washing buses and does not apply and compare the results of different multicriteria decision-making (MCDM) methods for the problem. The unique information available is from the different suppliers in the market. Whereby, this work intends to give a technical-scientific contribution to fulfill this gaps. Therefore, the main objectives of this work are (1) to select the best sustainable technical solutions for washing buses depending on the specific conditions for a case study and (2) to analyze how different multicriteria decision-making methods behave in the selection process. To achieve these objectives, the problem was approached as a case study in a public transport company in Portugal and the methodology followed the next steps: started with the identification of the different types of commercial technical solutions for washing buses; the company's experts selected four main criteria: water consumption, operating costs, quality of washing, and time spent; the criteria weights were determined using the fuzzy-AHP method; then four representative MCDM methods were selected, namely, AHP, ELECTRE, TOPSIS, and SMART; the ranks obtained for the four methods were compared; and a sensitivity analysis was performed. Considering the input data for the criteria and their weights, the results for all the methods showed that the best and the worst solution was the same, mobile portico with a brush and porticoes with three brushes, respectively. Furthermore, the results of the sensitivity analysis performed with disturbances for the weights of each criterion presented that the results are slightly affected and the similarity in rankings for the four MCDM methods was validated by Spearman's rank correlation coefficient (rs) and Kendall's coefficient of concordance (W). Considering these results, the SMART method, the less complex one, showed no difference from the others. For that reason, simple methods, such as SMART, in line with other works in the literature perform well in most cases. As a final remark of this work, it can be said that the methodology employed in this project can also be deemed applicable to other similar companies seeking technical solutions for bus or truck washing. Furthermore, the application of the SMART method, the less complex one and the most understandable for people, showed no difference from the others, being able to be applied in similar situations.

Abstract
Renewable energy and electric mobility are crucial in addressing current environmental and energy challenges. As the number of electric vehicles increases, more charging infrastructure connected to the electricity distribution network is required. This paper proposes an approach to sizing a fast charging station for electric vehicles. This challenge is addressed by including a battery energy storage system (BESS) and considering the self-production from a renewable energy source (solar energy) in the system. The aim is to minimise the total energy costs, avoid future infrastructure upgrades, and take advantage of the integration of renewable energy resources. The methodology used is a Biased Random Key Genetic Algorithm (BRKGA) based meta-heuristic. Computational experiments were conducted for the sizing of a charging station under four different scenarios that minimise energy costs. The results show that incorporating BESS can lead to a significant reduction in the costs related to the purchase of energy from the grid. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
In today's competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.