Abstract
The efficient last-mile delivery of goods involves complex challenges in optimizing driver sectors and routes. This problem tends to be large-scale and involves several criteria to meet simultaneously, such as creating compact sectors, balancing the workload among drivers, minimizing the number of undelivered packages and reducing the dissimilarity of sectors on different days. This work proposes a Decision Support System (DSS) that allows decision-makers to select improved allocation strategies to define sectors. The main contribution is an interactive DSS tool that addresses a many-objective (more than 3 objectives) sectorization problem with integrated routing. It establishes a global allocation strategy and uses it as a benchmark for the created daily allocations and routes. A Preference-Inspired Co-Evolutionary Algorithm with Goal vectors using Mating Restriction (PICEA-g-mr) is employed to solve the many-objective optimization problem. The DSS also includes a visualization tool to aid decision-makers in selecting the most suitable allocation strategy. The approach was tested in a medium-sized Metropolitan Area and evaluated using resource evaluation metrics and visualization methods. The proposed DSS deals effectively and efficiently with the sectorization problem in the context of last-mile delivery by producing a set of viable and good-quality allocations, empowering decision-makers in selecting better allocation strategies. Focused on enhancing service efficiency and driver satisfaction, the DSS serves as a valuable tool to improve overall service quality.

Abstract
Assembly lines are at the core of many manufacturing systems, and planning for a well-balanced flow is key to ensure long-term efficiency. However, in flexible configurations such as Multi-Manned Assembly Lines (MMAL), the balancing problem also becomes more challenging. Due to the increased relevance of these assembly lines, this work aims to investigate the MMAL balancing problem, to contribute for a more effective decision-making process. Therefore, a new approach is proposed based on Deep Reinforcement Learning (DRL) embedded in a Digital Twin architecture. The proposed approach provides a close-to-reality training environment for the agent, using Discrete Event Simulation to simulate the production system dynamics. This methodology was tested on a real-world instance with preliminary results showing that similar solutions to the ones obtained using optimization-based strategies are achieved. This research provides evidence of success in terms of dynamic resource assignment to tasks and workers as a basis for future developments.

Abstract
Participatory processes are an essential aspect of collaborative planning and decision-making processes, but designing such processes effectively can be quite challenging. This work departs from the assumptions that in sustainable urban mobility planning, the functional urban area needs to be considered, and that citizen engagement is often enacted at the neighborhood level. Under these assumptions, we have examined the experiences of 6 metropolitan cases (Bologna, Nantes, Manchester, Montreal, Christchurch, and Santiago de Chile) and draw insights from their experiences. We conclude this work with some general reflections on the importance of systemic approaches to effectively plan for sustainable transitions in urban mobility.

Abstract
The evolution of urban morphology and urban mobility reveals a complex and multidimensional relation that has been historically linked to the evolution of technology and its influence on people's behaviour, desires, and needs. The increasing level of digitalization of human interactions in both social and work environments has created a new paradigm for urban mobility. Alongside, sustainability concerns are also accelerating the design of new policies for improving citizens' quality of life in urban areas. To address this new paradigm, municipalities need to consider new methodologies encompassing the different dimensions of the urban environment. This can be achieved if key stakeholders participate in co-creating and co-designing new solutions for urban mobility. In this paper we propose a multidisciplinary approach to these problems, supported by service-dominant logic concepts. The approach was used to design the CoDUMIS framework that brings together four dimensions of urban areas (social, urban, technological, and organizational). The application of the framework to four distinct cases, in Portuguese municipalities, resulted in a set of guidelines that help municipalities to improve their services and policies in a participatory environment, involving all the stakeholders.

Abstract

Abstract
Organizations can transform their businesses and create more value by adopting Industry 4.0 initiatives. During evaluating these projects, the decision-maker must assess significant uncertainties (risks) resulting from socio-technical, economic, and financial factors. One of the main objectives of this study was to identify the necessary building blocks to develop a framework for project implementation in high-risk scenarios, as in the case of Industry 4.0. A multi-criteria framework divided into three stages was proposed, integrating knowledge from Front-End-Innovation (FEI), Innovation Decision Process (IDP), Traditional Project Evaluation Methods, and Real Options Valuation (ROV). The first step is to identify an investment opportunity. The second step is the definition of a business model. The third step is the simulation of different implementation strategies to give managerial flexibility to decision-makers to decide the best strategy to mitigate risks. A real case study was used to test the framework. According to the results, managers can use this framework to create different project implementation scenarios and determine the best strategy to mitigate risks. However, we must still understand whether uncertainties behave discretely, dynamically, or both, the interactions between elements, and how to calculate them to improve our model.