Abstract
Industry 4.0 is transforming manufacturing environments, with robotics being a key technology that enhances various capabilities. The flexibility of Autonomous Mobile Robots has led to the rise of multi-robot systems in industrial settings. Considering the high cost of these robots, it is essential to determine the best fit of number and type before making any major investments. Simulation and modeling are valuable decision-support tools, allowing the simulation of different setups to address robot fleet sizing issues. This paper introduces a decision-support framework that combines a fleet manager software stack with the FlexSim simulator, helping decision-makers determine the most suitable mobile robots fleet size tailored to their needs. Unlike previous approaches, the developed solution integrates the same real robot coordination software in both simulation and actual deployment, ensuring that tested scenarios accurately reflect real-world conditions. A case study was conducted to evaluate the framework, involving multiple tasks of loading and unloading materials within a warehouse. Five different scenarios with varying fleet sizes were simulated, and their performances assessed. The analysis concluded that, for the case study under consideration, a fleet of three robots was the most suitable, considering relevant key performance indicators. The results confirmed that the developed solution is an effective alternative for addressing the problem and represents a novel technology with no prior state-of-the-art equivalents.

Abstract
The advent of Industry 4.0, driven by automation and real-time data analysis, offers significant opportunities to revolutionize manufacturing, with mobile robots playing a central role in boosting productivity. In smart job shops, scheduling tasks involves not only assigning work to machines but also managing robot allocation and travel times, thus extending traditional problems like the Job Shop Scheduling Problem (JSSP) and Traveling Salesman Problem (TSP). Common solution methods include heuristics, metaheuristics, and hybrid methods. However, due to the complexity of these problems, existing models often struggle to provide efficient optimal solutions. Machine learning, particularly reinforcement learning (RL), presents a promising approach by learning from environmental interactions, offering effective solutions for task scheduling. This systematic literature review analyzes 71 papers published between 2014 and 2024, critically evaluating the current state of the art of task scheduling with mobile robots. The review identifies the increasing use of machine learning techniques and hybrid approaches to address more complex scenarios, thanks to their adaptability. Despite these advancements, challenges remain, including the integration of path planning and obstacle avoidance in the task scheduling problem, which is crucial for making these solutions stable and reliable for real-world applications and scaling for larger fleets of robots.

Abstract
Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path planning being key. The Time Enhanced A* (TEA*) algorithm extends A* by adding time as a dimension to resolve temporal conflicts in multi-robot coordination. However, inconsistencies in edge lengths within the graph can hinder optimal path calculation. To address this, a Graph Decomposition submodule was developed to standardize edge lengths and temporal costs. Integrated into a ROS-based fleet coordination system, this approach significantly reduces execution time and improves coordination capacity.

Abstract
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

Abstract
The reliability and robustness of cyber-physical systems (CPS) are critical aspects of the current industrial landscape. The high level of autonomous and distributed components associated with a large number of devices makes CPS prone to faults. Despite their importance and benefits, traditional fault tolerance methodologies, namely local and/or centralized, often overlook the potential benefits of collaboration between cyber-physical components. This paper introduces a collaborative fault diagnosis methodology for CPS, integrating self-fault diagnosis capabilities in agents and leveraging collaborative behavior to enhance fault diagnosis. The contribution of this paper relay in propose a methodology for fault diagnosis for CPS, based on multi-agent system (MAS) technology as a backbone of infra-structure, highlighting the components, agent behavior, functionalities, and interaction protocols, to explore the benefits of communication and collaboration between agents. The proposed methodology enhance the accuracy of fault diagnosis when compared with local approach. A case study was conducted in a laboratory-scale warehouse, focusing on diagnosing drift, bias, and precision faults in temperature and humidity sensors. Experimental results reveal that the collaborative methodology significantly outperforms the local approach in fault diagnosis, as evidenced by performance improvements in diagnosis classification. The statistical significance of these results was validated using the Wilcoxon signed-ranks test for paired samples.

Abstract
In the era of Industry 4.0, fault tolerance is essential for maintaining the robustness and resilience of industrial systems facing unforeseen or undesirable disturbances. Current methodologies for fault tolerance stages namely, detection, diagnosis, and recovery, do not correspond with the accelerated technological evolution pace over the past two decades. Driven by the advent of digital technologies such as Internet of Things, cloud and edge computing, and artificial intelligence, associated with enhanced computational processing and communication capabilities, local or monolithic centralized fault tolerance methodologies are out of sync with contemporary and future systems. Consequently, these methodologies are limited in achieving the maximum benefits enabled by the integration of these technologies, such as accuracy and performance improvements. Accordingly, in this paper, a collaborative fault tolerance methodology for cyber-physical systems, named Collaborative Fault * (CF*), is proposed. The proposed methodology takes advantage of the inherent data analysis and communication capabilities of cyber-physical components. The proposed methodology is based on multi-agent system principles, where key components are self-fault tolerant, and adopts collaborative and distributed intelligence behavior when necessary to improve its fault tolerance capabilities. Experiments were conducted focusing on the fault detection stage for temperature and humidity sensors in warehouse racks. The experimental results confirmed the accuracy and performance improvements under CF* compared with the local methodology and competitiveness when compared with a centralized approach.