Abstract

Abstract
This work addresses a variant of the job shop scheduling problem in which jobs need to be transported to the machines processing their operations by a limited number of vehicles. Given that vehicles must deliver the jobs to the machines for processing and that machines need to finish processing the jobs before they can be transported, machine scheduling and vehicle scheduling are intertwined. A coordi-nated approach that solves these interrelated problems simultaneously improves the overall performance of the manufacturing system. In the current competitive business environment, and integrated approach is imperative as it boosts cost savings and on-time deliveries. Hence, the job shop scheduling problem with transport resources (JSPT) requires scheduling production operations and transport tasks simultane-ously. The JSPT is studied considering the minimization of two alternative performance metrics, namely: makespan and exit time. Optimal solutions are found by a mixed integer linear programming (MILP) model. However, since integrated production and transportation scheduling is very complex, the MILP model can only handle small-sized problem instances. To find good quality solutions in reasonable com-putation times, we propose a hybrid particle swarm optimization and simulated annealing algorithm (PSOSA). Furthermore, we derive a fast lower bounding procedure that can be used to evaluate the perfor-mance of the heuristic solutions for larger instances. Extensive computational experiments are conducted on 73 benchmark instances, for each of the two performance metrics, to assess the efficacy and efficiency of the proposed PSOSA algorithm. These experiments show that the PSOSA outperforms state-of-the-art solution approaches and is very robust.(c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Abstract
PurposeThis study aims at validating a scale for the perception of competences and attitudes of the Portuguese public administration employees. The sample of this study consists of 1,119 participants working in public administration and other labour sectors in Portugal. The psychometric qualities of this instrument were studied to assess its use in future studies. Design/methodology/approachAn initial exploratory factor analysis showed that the scale is composed of one factor, with a Kaiser-Meyer-Oklin value of 0.83. The subsequent confirmatory factor analysis performed in AMOS 27 confirmed the existence of a single factor. FindingsThe analysis of the psychometric qualities of the scale allows concluding that it can be applied in the context of the Portuguese public administration. Originality/valueGiven the universality of the competences and attitudes adopted, it can be extended to other work and cultural contexts.

Abstract
Directed Energy Deposition (DED) is crucial in additive manufacturing for various industries like aerospace, automotive, and biomedical. Precise temperature control is essential due to high-power lasers and dynamic environmental changes. Employing Reinforcement Learning (RL) can help with temperature control, but challenges arise from standardization and sample efficiency. In this study, a model-based Reinforcement Learning (MBRL) approach is used to train a DED model, improving control and efficiency. Computational models evaluate melt pool geometry and temporal characteristics during the process. The study employs the Allen-Cahn phase field (AC-PF) model using the Finite Element Method (FEM) with the Multi-physics Object-Oriented Simulation Environment (MOOSE). MBRL, specifically Dyna-Q+, outperforms traditional Q-learning, requiring fewer samples. Insights from this research aid in advancing RL techniques for laser metal additive manufacturing.

Abstract
The protection of agri-food regional products is taking on growing importance in a market dominated by global companies and brands, often with no personality. Thirty years ago, the European Union (EU) agricultural product quality policy introduced the protection of geographical indications (GIs) for agricultural products and foodstuffs, with the aim of highlighting the quality of products resulting from a specific origin, therefore helping their communication and positioning in the market. This is important in countries with a considerable percentage of rural regions, as is the case of Portugal. Bearing this in mind, the purpose of this study is to see what are the drivers of the spatial distribution of traditional products (protected geographical indications, protected designations of origin, and traditional speciality guaranteed) in Portugal. For this purpose, the distribution of traditional products by regions and categories in Portugal will be presented. Also, Portugal's position will be analysed and compared to the other EU countries, regarding the number of traditional products. Results show that Portugal is the country with the fourth biggest number of traditional certified products in EU territory. In the national territory, the Northern Region of Portugal has the biggest percentage of protected products, followed by Alentejo and the Centre Region of Portugal. Also, in Portugal, looking at the type of products, from a list of ten different categories of GIs, the ranking is dominated by (1) fresh meat, (2) meat products (cooked, salted, or smoked), and (3) cheese and milk-based products. If we consider that many of the aforementioned products are produced in less favoured regions, these results constitute an opportunity for their sustainable development. This benefits not only the producers, but also consumers who increasingly seek authentic and more natural products.

Abstract
Power systems are evolving very rapidly namely in what concerns the technologies used to generate electricity, the diversification of commercial relationships involving different agents and more specifically the empowerment of consumers. In this scope, several countries passed new legislation to induce the installation of Renewable Energy Communities, RECs, to induce new investments at a local level, to empower end consumers and to increase their self-sufficiency. However, the way Local Energy Markets, LEMs, will be integrated into Wholesale Markets, WSM, is not yet fully established. To this end, this paper proposes a design and an optimization model to increase the mentioned self-sufficiency level, to better manage the energy produced locally, also admitting the installation of battery storage units, and to profit as much as possible of them. LEM interaction with WSM, is based on an Agent Based Model architecture equipped with a Q-learning strategy. An economic assessment is also included, in order to get insights if some level of exemption, for instance associated with some components of the Access Tariffs, have to be considered in order to induce the massification of RECs.