Load Balancing Optimization of Logistics Hub Network Based on User's Preference Behavior

Considering the effects of different size discount policies and distances on users' hub selection preferences, this paper proposes a workload balancing design method for logistics transit hub networks based on users' finite rational preference behavior to address the hub load imbalance caused by users' preference behavior in logistics hub networks. First, a polynomial Logit rule with constraints under incomplete information is used to simulate users' preference behavior under limited rationality, and a multi-objective optimization model is constructed to minimize the generalized cost and maximize the temporal utility, including hub low load utilization and congestion penalty cost, with hub location and transportation routes as decision variables. To address this problem, a hybrid algorithm framework is developed, which first divides the allocation decision space according to the Leuven algorithm to reduce the difficulty of the solution, and then adds multiple population mechanism and cooperative search strategy based on the non-dominated genetic algorithm (NSGAⅡ) to improve the convergence ability of the algorithm. At last, the effectiveness of the model and algorithm is verified by taking Guangxi logistics network as an example. The results show that the load of the hub network is more balanced under the fully rational condition, and the user's preference behavior will aggravate the phenomenon of hub load imbalance, leading to the increase of the generalized cost and time consumption of the hub network. Considering the preference behavior of customers under finite rationality, the load balancing capability of the entire hub network under the active discount scheme performs better than that of the general discount scheme and close to the load balancing capability of the fully rational state of users. The average load ratio and average congestion rate are respectively 60.17% and 34.40%. With the consideration of the user preferences, the load ratio of the hub increases with the increase of discounts, which also makes the congestion rate increase. The designed hybrid evolutionary algorithm converges to a more balanced objective value, exhibits strong search and optimization performance, and can obtain an effective solution to the model. © 2024 Science Press. All rights reserved.