In the last decade, simulation software as a tool for managing and controlling business processes has received a lot of attention. Many of the new software features allow businesses to achieve better quality results using optimisation, such as genetic algorithms. This article describes the use of modelling and simulation in shipment and sorting processes that are optimised by a genetic algorithm’s involvement. The designed algorithm and simulation model focuses on optimising the duration of shipment processing times and numbers of workers. The commercially available software Tecnomatix Plant Simulation, paired with a genetic algorithm, was used for optimisation, decreasing time durations, and thus selecting the most suitable solution for defined inputs. This method has produced better results in comparison to the classical heuristic methods and, furthermore, is not as time consuming. This article, at its core, describes the algorithm used to determine the optimal number of workers in sorting warehouses with the results of its application. The final part of this article contains an evaluation of this proposal compared to the original methods, and highlights what benefits result from such changes. The major purpose of this research is to determine the number of workers needed to speed up the departure of shipments and optimise the workload of workers.

In the current global economy, where rapid changes and constantly shifting market demands define the competitive landscape, adaptive manufacturing systems become essential for businesses striving to remain relevant and efficient. In the context of this growing need, this study focuses on planning as a part of adaptive manufacturing system. This methodology provides a systematic framework that spans from foundational groundwork to meticulous verification and validation phases. By employing advanced simulation techniques, seamless data integration, and process optimization, this methodology ensures the smooth realization of robust and efficient adaptive manufacturing systems. A detailed case study on competency islands showcases the versatility of this approach, demonstrating its efficacy in enhancing manufacturing agility and overall performance. As a significant contribution to the field of smart manufacturing, this methodology offers a structured blueprint for the realization of adaptive manufacturing systems.