Scheduling of transportation fleet based on the customer’s priority in a hub location problem

Abstract

This study proposes six novel strategies 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 nonstatistical 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.

The Multi-Objective Model of the Hub Covering Location-Routing Problem with Simultaneous Delivery and Pickup Under Uncertainty

“…Finally, the proposed models were analyzed using the famous Australia Post dataset. Motamedi et al (2023) used six new techniques in prioritizing customers in the hub optimization problem. In these techniques, each strategy assigns a value to each customer based on distance and demand parameters, where customers are prioritized based on this value.…”