人工蜂群算法在LRIP机会约束模型中的应用

Artificial Bee Colony Algorithm for Chance-constrained Model of LRIP

针对现有研究中未考虑配送阶段客户随机需求的问题,本文采用在一定置信区间上满足客户需求的方法,描述这种客户需求不确定的约束,在此基础上,建立了选址-路径-库存问题(Location-Routing-Inventory Problem,LRIP)的机会约束模型。提出人工蜂群算法(Artificial Bee Colony algorithm,ABC)对该问题模型进行优化求解。结合问题特征和邻域知识,提出了一种基于矩阵的编码方法,构造了启发式初始化方法,设计了2种基于矩阵编码的交换策略,在此基础上构造了5种蜂群搜索算子。通过仿真实验,分析比较了初始化方法和5种搜索策略;同时将人工蜂群算法与两阶段法进行了比较,优化结果证明人工蜂群算法是求解LRIP问题的有效方法。

As the uncertain demand of customers in the delivery stage has not been considered in the current research, it is proposed to use the method that the loading capacity of the vehicle meets the demand of customers at a certain confidence interval to express the uncertainty constraint in the paper. Then a chance-constrained model of Location-Routing-Inventory Problem （LRIP）is established. Furthermore, the artificial bee colony algorithm （ABC） is applied to solve the model. According to the characteristics of the LRIP and the knowledge of the solution＇s domain structure, a new encoding method based on matrix is proposed. Base on this, a heuristic initialization method is established, and two kinds of exchange policies based on the matrix coding are designed. Additionally, five kinds of artificial bees search strategies are proposed. Finally, the initialization methods and the five kinds of artificial bees search strategies are compared in the experiments. The artificial bee colony algorithm with the two-stage method is also compared. The simulation results show that the artificial bee colony algorithm is an effective algorithm to solve the LRIP.