Continuously maintaining a logistics network (LNW) in good condition is a challenging task for decision makers. For purposes of improving an LNW's performance, promising actions need to be identified, such as the centralization of a stock keeping unit (SKU). In order to support the decision maker, the authors have developed a logistics assistance system (LAS) based on discrete-event simulation. With an increasing size of the LNW, the response time of such an LAS increases exponentially. In this paper, the authors present an approach for utilizing domain-specific information to guide the search for promising actions and, therefore, reduce the LAS's response time. The given examples show that the LAS's response time can be decreased. For example, the approach reduces the number of iterations needed by an evolutionary algorithm to converge.