Abstract
In this paper, a decision support application, for the air cargo planning and distribution, is proposed. The freight forwarding sector has been working to be assertive and efficient in responding to the market through an efficient approach to planning and allocation problems. The main goal is to minimize costs and improve performance. A real air cargo distribution problem for a freight forwarder was addressed. This project emerged from the need to efficiently plan and minimize costs for the distribution of thousands of m(3) (cubic meters) of air cargo, while considering the market restrictions, such as aircraft availability and transportation fees. Through the GRG algorithm adaptation to the real problem, it was possible to respond to the main goal of this paper. The development of an easy-to-use application ensures a quick response in the air distribution planning, focusing on cost reduction in transportation. With the application development it is possible to obtain real earnings with immediate effect.

Abstract
The present study consists in the comparison of two metaheuristics in a scheduling problem (SP), in particular in the minimization of the makespan in flowshop problem. The two selected metaheuristics were DABC (Discrete Artificial Bee Colony) and ACO (Ant Colony Optimization). For the performance analysis, the metaheuristics were tuned with an extensive DOE study, subsequently, several tests were performed. Thirty-one evenly distributed instances were generated for a in-depth analysis and each one was subjected to three runs for each metaheuristic. Through the results obtained, it was possible to concluded that the DABC has a better performance when compared to SA and ACO. SA and ACO have a similar performance in the chosen problem. These conclusions were supported by descriptive statistics and statistical inference. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
In the context of the COVID-19 pandemic, public spaces had to be quickly adapted to the new circumstances especially under the uncertainty of the pandemic development. Door handles are some of the most touched surfaces and so, this point of contagion was chosen to be tackled and two solutions were developed that would prevent direct touch with the handle: a portable and a fixed device. The portable device (HYHOOK + HYTIP) is a hook-like device holding a finger cover, which permits to open doors and push buttons safely. The fixed device (HANDGENIC) is meant to be assembled in door handles to equip buildings, such as universities or schools. With the fixed device, the user can open the door using their forearm which makes them less likely to transfer any particles to eyes, nose or mouth. The 3D printing Fused Filament Fabrication (FFF) process was selected as manufacturing technique, which allows the fast production of prototypes. This work portrays the development process and design iterations taking into consideration the concerns about the functioning of the devices and possible failures or alternative uses. To assure structural integrity of the parts, finite element (FE) analysis was used to verify its mechanical response. As conclusion, it was found that FE analysis indicate that the devices are structurally sound to be used in public spaces and that 3D printing is a useful way to rapidly develop devices while testing several design possibilities.

Abstract
Effective project management is crucial to the success of any industry, particularly in metalworking, where deadlines, resources, and costs play critical roles. However, accurately predicting project execution times remains a significant challenge, directly impacting companies' competitiveness and profitability. In this context, the integration of Artificial Intelligence (AI) tools emerges as a promising solution to improve the accuracy of time predictions and optimise project management in the metal-working industry.AI, particularly through techniques such as Machine Learning (ML), has demonstrated significant potential in predicting timeframes for engineering projects. Predictive activity-based models can be trained with historical data to identify patterns and forecast future durations with high accuracy. In the metalworking sector, where projects are often complex and subject to variability, AI can provide notable advantages in terms of precision and efficiency.This study aims to formulate an activity-based model, represented in IDEF0 (part of the Integration Definition for Function Modelling), for predicting activity durations using AI to support project management in the metalworking industry. By applying the principles of the IDEF0 tool, the objective is to develop a robust and adaptable system capable of analysing historical data, environmental factors, project characteristics, and other relevant inputs to produce more accurate time forecasts.With this work, we aim to contribute to the advancement of Project Management (PM) in the metal-working industry, particularly by providing an activity-based model to support the creation of an innovative AI tool for predicting execution times with greater accuracy.

Abstract
In recent decades, with globalization, the financial crisis and the entry of new players in the global markets, the industrial enterprises in order to survive are forced to update its supply chain strategy in line with the new and challenging circumstances. Thus, company managers have put a growing interest in developing strategies designed to provide companies with the resources, capabilities and ICT technologies in order to compete successfully in the market. Supply chain managers increasingly understand that it is the time to fundamentally rethink how to design and operate the supply chains that link with their own company, namely suppliers and customers. For too long, there has been a major concern with infrastructures, resource utilization and cut costing in the operation activities, ultimately in efficiency. However, even recognizing that maintaining lower levels of operating costs are important, achieving and maintaining future competitiveness demands more sophistication.

Abstract
This work seeks to determine the criteria and methods used in the problem of selecting suppliers, thus contributing to the support of entities wishing to start a selection of suppliers more effectively. To achieve these objectives, an analysis was performed of articles that make the literature review of the methods and criteria from the year 1985 to the year 2012. With the data obtained from these reviews, it was possible to verify which are the three main methods used over the years, namely data envelopment analysis (DEA), Analytical hierarchy process (AHP) and Fuzzy set theory and the main criteria used in the selection of suppliers. In this work, we present an overview of the decision making and the methods used in multi-criteria decision making. It’s tackled the problem of supplier selection, the process of selection and the reviews of literary methods and criteria used in recent years. Finally is presented the contribution to the selection of suppliers of the study conducted during the development of this dissertation, being presented and explained the main methods of selection of suppliers as well as the criteria used.