基于用户偏好行为的物流枢纽网络负载均衡优化

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

针对物流转运枢纽网络中用户偏好行为导致的枢纽负载失衡现象,本文考虑不同的规模折扣政策和距离对于用户枢纽选择偏好的影响,提出一种基于用户有限理性偏好的物流转运枢纽网络负载均衡设计方法。设计不完全信息下带有约束的多项式Logit规则模拟用户有限理性下的偏好行为,以枢纽位置和运输路径为决策变量,构建以包含枢纽低负载利用和拥堵的惩罚成本在内的最小广义成本和最大化时间效用的多目标优化模型;设计具有双编码结构染色体的混合进化算法框架,通过鲁汶算法对分配决策空间进行分区,以非支配遗传算法作为算法主框架(NSGAⅡ),设计多种群机制和种群内双向协同搜索策略,提升算法对解空间的搜索能力;并以广西物流运输网络为例,验证模型及算法的有效性。结果表明:在完全理性状况下的枢纽网络负载较为均衡,用户的偏好行为会加剧枢纽负载失衡的现象,导致枢纽网络的广义成本和时间耗费上升;而在考虑物流用户有限理性下的偏好行为状况下,相对于一般的折扣方案,积极折扣方案下的整体枢纽网络的负载均衡能力较优,且接近于理性状态下的网络负载均衡能力,其中,平均负载率和平均拥堵率分别为60.17%和34.40%;在考虑用户偏好的状态下,随着折扣力度的增强,枢纽的负载率上升,同时,拥堵率上升;本文设计的混合进化算法收敛到的目标值更为均衡,表现出较强的搜索和寻优性能,能够实现有效求解该模型。

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.