Abstract
Effective warehouse management plays a pivotal role in optimizing supply chain performance, particularly in high-demand, time-sensitive environments. This study introduces a simulation-based decision support system designed to improve the management of Picking-By-Line (PBL) operations in cross-docking distribution centres. Developed in FlexSim and calibrated with empirical data from an industrial case study, the model replicates real-world warehouse conditions and is validated against observed operational performance. The tool supports warehouse managers in evaluating and comparing operational strategies, such as dynamic storage allocation policies and picker routing constraints, with the goal of reducing operator travel distances, mitigating congestion, and enhancing overall efficiency. A key contribution of this work is the integration of congestion-sensitive performance indicators that allow for a detailed analysis of the trade-offs between travel efficiency and localized congestion-an aspect often overlooked in traditional optimization methods. This study demonstrates the value of simulation as a scalable and realistic decision-support tool for optimizing PBL operations in complex and variable environments where human movement is a major cost and performance driver. The proposed tool bridges the gap between theoretical modelling and practical implementation, offering actionable insights for warehouse layout, space utilization, and resource allocation.

Abstract
The rapid advancement of warehouse automation has increased the need for intelligent intralogistics solutions that enhance material handling efficiency and optimize space utilization. This research presents a simulation-based methodology that integrates Autonomous Mobile Robots (AMRs) with container loading optimization in a unified decision-support framework that dynamically synchronizes AMR routing with optimized truckload configurations, a feature not commonly addressed jointly in existing literature to improve warehouse operations. By leveraging a hybrid approach combining discrete event and agent-based simulation in FlexSim, the study evaluates the impact of AMR fleet size, routing strategies, and truckload configurations on overall logistics performance. A proof-of-concept industrial case study illustrates how different scenarios influence key performance metrics, such as total operation time and resource utilization. The findings demonstrate that synchronized AMR deployment and optimized container loading strategies contribute to increased throughput, reduced handling time, and enhanced logistics unit utilization. This work provides a framework for dynamic logistics planning, offering valuable insights for companies seeking to enhance warehouse efficiency and sustainability through simulation-driven decision support. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The adoption of Autonomous Mobile Robots (AMRs) has emerged as a promising solution to enhance efficiency and reduce operational costs for industrial companies. Given the significant cost of AMRs, it is crucial to determine the optimal number and characteristics before making significant investments. This study proposes a decision-support framework based on simulation to assess the impact of integrating AMR robots in a complex distribution center. Additionally, this framework aids decision-makers in determining the optimal fleet size of AMR robots and corresponding charging stations. A simulation model was developed using data from a leading retail company, focusing on pallet movement within the facility, comparing scenarios combining AMRs with other intralogistics implementations. This methodology incorporates uncertainty, variability (statistical distributions to create transportation orders, acceleration, demand and offer fluctuations) and implements fleet management, transportation capacity, demand matching, and resource utilization according to real case scenarios. The proposed model replicates accurate robot coordination and actual deployment environments, ensuring that the tested scenarios approximate the real-world conditions as much as possible. Preliminary findings show results supporting the decision-making for a fleet size to meet weekly production targets, optimize robot utilization, and coordinate charging instances to prevent production stops. Conclusions suggest that the proposed simulation approach is an effective tool for planning and implementing logistics solutions, enabling users to make informed decisions before investing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
The Maintenance, Repair and Overhaul (MRO) activities in the aviation industry face constant challenges due to the uncertainty and variability of their operations. Aircraft engine maintenance, which is fundamental to the safety of aircraft operations, is particularly challenging due to its job-shop nature. Each engine requires a specific intervention process, based on its condition and the needs identified. The inherent uncertainty in task duration, resource availability, and the scope of required repairs adds complexity to capacity planning. Traditional capacity planning methods often fall short in accounting for these uncertainties, leading to potential inefficiencies and bottlenecks. Discrete Event Simulation (DES) emerges as a powerful tool to address these challenges. By modelling the entire MRO process, DES can consider various scenarios, incorporating the stochastic nature of task times, machine downtimes, and labour availability. This study explores the application of DES to evaluate capacity planning and quantify the impact of uncertainty on operational efficiency. The proposed methodology enables the anticipation of delays and enhances resource management. The primary contribution of this work is the ability to predict delays and quantify their impact. The future application of this tool in real-world MRO operations has the potential to enhance operational efficiency and reliability. © 2025 Elsevier B.V., All rights reserved.

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.