Bi-objective Hub Location-Allocation Problem with Time Window Constraint

This paper proposed a bi-objective programming model to address the hub location-allocation problem international logistics enterprises faced. The model considers high time requirement of express export business and includes a time window constraint that considers the sum of transport vehicle departure time, vehicle driving time, sorting time of service center, and the latest arrival time of vehicles at the port. The model aims to minimize the total transportation cost and construction costs while maximizing the service level. Firstly, to predict vehicle transportation costs, a binary regression equation was constructed using distance and volume as independent variables, based on the sklearn machine learning library. The resulting mixed integer programming model was then solved using the Gurobi solver. Using operational data from international logistics enterprises in Guangdong Province, two site selection schemes were obtained, prioritizing either total cost or service level. The results of the study indicate that the location scheme prioritizing total cost can effectively reduce costs, while the scheme prioritizing service level can ensure that the service level remains unaffected by the addition of new service centers.