Catarina Moreira Marques

Abstract
This paper examines the role of digital twins (DTs) in promoting sustainability within seaport operations and logistics. DTs have emerged as promising tools for enhancing seaport performance. Despite the recognized potential of DTs in seaports, there is a paucity of research on their practical implementation and impact on seaport sustainability. Through a systematic literature review, this study seeks to elucidate how DTs contribute to the sustainability of seaports and to identify future research and practical applications. We reviewed and categorized 68 conceptual and practical digital applications into ten core areas that effectively support economic, social, and environmental objectives in seaports. Furthermore, this paper proposes five preliminary potential applications for DTs where practical implementations are currently lacking. The primary findings indicate that DTs can enhance seaport sustainability by facilitating real-time monitoring and decision-making, improving safety and security, optimizing resource utilization, enhancing collaboration and communication, and supporting the development of the seaport ecosystem. Additionally, this study addresses the challenges associated with DT implementation, including high costs, conflicting stakeholder priorities, data quality and availability, and model validation. The paper concludes with a discussion of the implications for seaport managers and policymakers.

Abstract

Abstract
By connecting maritime and land transport, container terminals play a critical role in global logistics systems, as part of broader intermodal networks. The evolution of containerisation and technological advances, along with increased demand and volumes, led to significant adaptations in these terminals, as a way to improve productivity, reduce costs and increase competitiveness, while coping with spatial and operational constraints. For strategic decision-making, managing these complex systems can be enhanced by simulation models allowing the analysis of different scenarios in dynamic, uncertain environments. This work, presents a simulation-based decision support tool developed in the FlexSim software, to analyse different container terminal configurations, with a particular focus on automation and on sustainable practices to reduce the energy consumption of terminals. A discrete event simulation model was developed to study multiple scenarios impacting productivity, resource utilisation, and waiting times. The proposed approach allows the test and evaluation of management strategies for port operations, with preliminary results showing that sizing and planning of the fleets of automated guided vehicles (AGV) can significantly affect the total operating time, the energy consumed, and the costs associated with battery charging operations. Future research should explore additional factors affecting container terminal operations, such as the reorganisation of the storage area, while incorporating optimisation elements for work planning and resource allocation. Moreover, the simulation model will be tested and validated in a real case study, designed for the Port of Sines in Portugal. © 2024 The Authors.

Abstract
This research addresses the Frequency Setting Problem (FSP) together with vehicle technology selection for bus fleet sizing and management. A decision support tool was developed that combines a multi-criteria decision analysis, using the Analytic Hierarchy Process (AHP), and an enumeration procedure. The tool assists transportation operators in selecting optimal frequencies and vehicle technologies, considering economic, social, and environmental criteria. Computational experiments performed in the city of Niterói, Brazil, demonstrate the effectiveness of the tool. Scenarios with different criteria prioritizations highlight the flexibility of the approach and emphasize the need for a balance between all the sustainability dimensions. This approach positively impacts public transportation system performance, favouring higher-capacity vehicles while considering demand, and contributing to sustainable urban mobility. © 2024 The Authors.

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.