Decision makers (DMs) for logistics networks (LNWs) have the complex task of maintaining their networks in good conditions while internal and external demands are changing. Therefore, the DMs need to identify promising actions in order to adapt to the LNW's changing state, e.g., increasing the stock level of stock keeping units (SKUs). The authors have developed a logistics assistance system (LAS) that automatically alters the LNW's model, for improving it under changing conditions, by applying actions and evaluating their effects on the LNW's performance. Promising actions are suggested to the DM. As the LNW grows in size, the number of potential actions increases and therefore, the response time of the LAS increases as well under the additional computational burden. In this paper, the authors describe a novel concept for reducing the number of actions by grouping similar actions together, leading to faster convergence and shorter response time of the LAS.