Abstract
The concept of Industry 4.0 promises to transversally revolutionise industries. Simulation, as one of the main pillars of Industry 4.0, allows improvements in the organisational and production processes of companies. This research work develops a decision support tool based on system dynamics, that address the problem of car dealership sales forecast and evolution depending on the commercial strategies adopted. This decision support tool considers main variables that are expected to influence car sales in Portugal. To develop this tool several interviews were conducted with the people responsible for the commercial sector of different dealerships while considering existing literature on the subject. This allowed us to parameterize a system dynamics model with the most influential sales factors. The developed tool is expected to contribute to car dealerships to evaluate their commercial policies and define adjustments to these to improve profitability.

Abstract
Demand Driven Material Requirements Planning (DDMRP) assumes that a production order is generated for replenishment when the inventory position, given by the net flow equation, is below a given level. Literature on this production planning and control system suggests prioritizing open orders on the shop floor based on the inventory buffer status. However, the performance of buffer-oriented priority dispatching largely remains unknown. Using discrete event simulation, this study suggests that buffer-oriented dispatching based on the net flow equation outperforms due date-oriented dispatching rules and first-come-first-served. The performance impact depends, however, on the reorder quantity associated with the production orders. These results have important implications for industrial practice.

Abstract
Total evacuation time constitutes an important factor in the safety of any building. It is thus essential to devise an emergency evacuation plan, which will enable the safe evacuation of all the occupants in the shortest possible time. The main objective of this article was to examine and improve the evacuation process of a 4-star hotel located in the city of Porto, Portugal. To this end, one looked into 6 scenarios, by means of PathFinder simulation software, so as to determine the shortest total evacuation time and identify possible bottlenecks and congestion. The simulation model developed was tested to analyze the evacuation of 429 people from the hotel, based on the availability of the 3 accessible exit doors (central exit, side exit, spa exit) and elevators. Strategy 4 presented the shortest total evacuation time, with 536.0 s. Two other strategies which showed very similar times were 5 and 6, with 537.0 s and 537.5 s, respectively.

Abstract
This paper studies a real world dedicated parallel machine scheduling problem with sequence dependent setups, different machine release dates and additional resources (PMSR). To solve this problem, two previously proposed models have been adapted and a novel objective function, the minimisation of the sum of the machine completion times, is proposed to reflect the real conditions of the manufacturing environment that motivates this work. One model follows the strip-packing approach and the other is time-indexed. The solutions obtained show that the new objective function provides a compact production schedule that allows the simultaneous minimisation of machine idle times and setup times. In conclusion, this study provides valuable insights into the effectiveness of different models for solving PMSR problems in real-world contexts and gives directions for future research in this area using complementary approaches such as matheuristics.

Abstract
This work was developed to improve the costing process of new products within the Product Development Department of a furniture manufacturer. It consisted of creating a parametric cost estimation model based on applying simple and multiple linear regressions, considering the existing data of the products produced and their respective costs. The proposed model considers the cost estimation of creating a product that covers the materials and operations costs. The suitability of the different independent variables was studied by applying simple and multiple linear regressions. A set of functions that return an estimate of the cost as a function of these predictor variables was obtained. The model built with the functions obtained provides the materials and operations cost estimation. The results indicated that 75% of the tests performed show an estimation error of less than 2% in the total cost of a product. Incorporating this model in a tool with the purpose of cost estimation brings the ability to predict prices faster, improving the internal process of obtaining costing and enhancing the analytical capacity of the team in the relentless pursuit of cost minimization and value creation.

Abstract
The field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.