This study proposes six novel techniques on the customer’s priority while addressing the conventional hub location issue. Each strategy assigns a value to every customer based on distance and demand parameters, in which customers are prioritized based on this value. Then, the vehicle fleet is scheduled according to the customer’s priority. A new mixed-integer linear programming model is presented and applied for each strategy in a new hub location-scheduling problem solved by three approaches. Then, by using the CAB dataset, extensive experiments are designed to evaluate each strategy. The strategies are evaluated with statistical and non-statistical analyses and ranked accordingly. In each case, a comparison of the non-priority strategy with the best customer’s prioritization strategy shows that the non-priority strategy has an adverse effect on the delivery time (i.e., 129.7%, 171.68%, and 161.33% than the best strategy in the case of near, medium, and far nodes, respectively). In addition to the above tests, other tests are conducted to evaluate the optimum number of vehicles for different conditions. The results show that as the distance between customers and hubs increases, reducing the number of vehicles while increasing their capacity is preferable. Also, each strategy requires using a certain number of vehicles.