Decision-makers face a challenging task in selecting decisions in logistics networks. For this purpose, logistics assistance systems (LASs) have been developed. These systems aim to improve the performance of a logistics network presented by performance measures, such as total costs and a service level. They support decision-makers in selecting actions to be applied on the networks, such as increasing the stock level of a stock keeping unit or centralising a stock keeping unit in a site. They can be based on a simheuristic approach that utilises simulation and metaheuristics in selecting actions and optimising the network. The metaheuristic algorithm selects actions, and the simulation evaluates their impact on the performance of the network. The logistics assistance systems ought to consider a vast number of actions when optimising large logistics networks. The huge number of actions challenges the LAS to recommend a promising list of actions to improve the performance of the network. Therefore, the LAS is guided to select promising actions to optimise the network. Domain-specific information related to the actions and the logistics networks are used to guide the search for promising actions. This presentation demonstrates the utilisation of correlation. The correlation is defined in the context of sequential actions and their impact on the performance of the network. Experimental results showed that correlation utilisation helped to guide the LAS to recommend promising actions.