Industrial manufacturing environments are often characterized as being stochastic, dynamic and chaotic, being crucial the implementation of proper maintenance strategies to ensure the production efficiency, since the machines’ breakdown leads to a degradation of the system performance, causing the loss of productivity and business opportunities. In this context, the use of emergent ICT technologies, such as Internet of Things (IoT), machine learning and augmented reality, allows to develop smart and predictive maintenance systems, contributing for the reduction of unplanned machines’ downtime by predicting possible failures and recovering faster when they occur. This paper describes the deployment of a smart and predictive maintenance system in an industrial case study, that considers IoT and machine learning technologies to support the online and real-time data collection and analysis for the earlier detection of machine failures, allowing the visualization, monitoring and schedule of maintenance interventions to mitigate the occurrence of such failures. The deployed system also integrates machine learning and augmented reality technologies to support the technicians during the execution of maintenance interventions.

Population in cities is growing worldwide, which puts the systems that offer basic services to citizens under pressure. Among these systems, the Municipal Solid Waste Management System (MSWMS) is also affected. Waste collection and transportation is the first task in an MSWMS, carried out traditionally in most cases. This approach leads to inefficient resource and time expense since routes are prescheduled or defined upon drivers’ choices. The waste collection is recognized as an NP-hard problem that can be modeled as a Capacitated Waste Collection Problem (CWCP). Despite the good quality of works currently available in the literature, the execution time of algorithms is often forgotten, and faster algorithms are required to increase the feasibility of the solutions found. In this paper, we show the performance of the open-source Google OR-Tools to solve the CWCP in Bragança, Portugal (inland city). The three metaheuristics available in this tool were able to reduce significantly the cost associated with waste collection in less than 2 s of execution time. The result obtained in this work proves the applicability of the OR-Tools to be explored for waste collection problems considering bigger systems. Furthermore, the fast response can be useful for developing new platforms for dynamic vehicle routing problems that represent scenarios closer to the real one. We anticipate the proven efficacy of OR-Tools to solve CWCP as the starting point of developments toward applying optimization algorithms to solve real and dynamic problems.

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development.

Manufacturing activities and production control are constantly growing. Despite this, it is necessary to improve the increasing variety of scheduling and layout adjustments for dynamic and flexible responses in volatile environments with disruptions or failures. Faced with the lack of realistic and practical manufacturing scenarios, this approach allows simulating and solving the problem of job shop scheduling on a production system by taking advantage of genetic algorithm and particle swarm optimization algorithm combined with the flexibility and robustness of a multi-agent system and dynamic rescheduling alternatives. Therefore, this hybrid decision support system intends to obtain optimized solutions and enable humans to interact with the system to properly adjust priorities or refine setups or solutions, in an interactive and user-friendly way. The system allows to evaluate the optimization performance of each one of the algorithms proposed, as well as to obtain decentralization in responsiveness and dynamic decisions for rescheduling due to the occurrence of unexpected events.

Home Health Care (HHC) services are growing worldwide and, usually, the home care visits are manually planned, being a time and effort consuming task that leads to a non optimized solution. The use of some optimization techniques can significantly improve the quality of the scheduling solutions, but lacks the achievement of solutions that face the fast reaction to condition changes. In such stochastic and very volatile environments, the fast re-scheduling is crucial to maintain the system in operation. Taking advantage of the inherent distributed and intelligent characteristics of Multi-agent Systems (MAS), this paper introduces a methodology that combines the optimization features provided by centralized scheduling algorithms, e.g. genetic algorithms, with the responsiveness features provided by MAS solutions. The proposed approach was codified in Matlab and NetLogo and applied to a real-world HHC case study. The experimental results showed a significant improvement in the quality of scheduling solutions, as well as in the responsiveness to achieve those solutions.