基于改进量子进化算法的末端配送任务动态分配模型

大多数物流快递企业的配送业务末端会按照固定的配送服务区进行配送任务分配,无法针对变化频繁、分布不均的动态配送需求进行合理的配送资源设置,造成了各个末端配送节点工作负荷不均衡的现象,并进一步导致了配送调度管理混乱等问题。针对末端配送任务分配问题建立了一种考虑配送成本,资源利用率以及工作量配比差异的配送任务分配模型,对量子进化算法进行改进。对此问题求解,提出采用量子群稳定度作为算法退出判定条件,来避免算法的早退与无效迭代问题,并引入量子变异与淘汰机制,加强了算法对可行解的搜索能力。实验结果表明,与按配送区进行分配的方案相比,算法给出的方案有效缓解了配送任务分配不均的现象,同时也有效降低了总体配送成本。相关模型和算法可以根据动态的配送需求合理地分配各个末端网点的配送任务,有助于配送业务的下一步配送路径优化和科学调度。

Terminal Distribution Task Assignment Optimization Model and Method Based on Improved Quantum Evolutionary Algorithm

The distribution service terminal of most logistics express enterprises will be assigned according to the fixed distribution service area, and the distribution resources can not be set up reasonably for the dynamic distribution requirements of frequent change and uneven distribution, which leads to the uneven load balance of all terminal distribution nodes, and further leads to the management chaos of distribution and other problems. The problem of chaotic scheduling management and so on. A distribution task allocation model, which considers distribution cost, resource utilization ratio and the difference of workload ratio, is set up to improve the quantum evolution algorithm. In order to solve this problem, a quantum group stability is proposed as an algorithm to exit the decision condition to avoid the early regression and invalid iteration of the algorithm, and the quantum mutation and elimination mechanism is introduced to strengthen the search ability of the algorithm for the feasible solution. The experimental results show that the proposed scheme effectively alleviates the uneven distribution of distribution tasks compared with the allocation scheme according to the distribution area, and reduces the overall distribution cost effectively. The related models and algorithms can allocate the distribution tasks of each end node reasonably according to the dynamic distribution requirements, and help to optimize the next distribution path and the scientific scheduling of the distribution business.