Order picking is one of the most demanding activities in many warehouses in terms of capital and labor. In parts-to-picker systems, automated vehicles or cranes bring the parts to a human picker. The storage assignment policy, the assignment of products to the storage locations, influences order picking efficiency. Commonly used storage assignment policies, such as full turnover-based and class-based storage, only consider the frequency at which each product has been requested but ignore information on the frequency at which products are ordered jointly, known as product affinity. Warehouses can use product affinity to make informed decisions and assign multiple correlated products to the same inventory “pod” to reduce retrieval time. Existing affinity-based assignments sequentially cluster products with high affinity and assign the clusters to storage locations. We propose an integrated cluster allocation (ICA) policy to minimize the retrieval time of parts-to-picker systems based on both product turnover and affinity obtained from historical customer orders. We formulate a mathematical model that can solve small instances and develop a greedy construction heuristic for solving large instances. The ICA storage policy can reduce total retrieval time by up to 40% compared to full turnover-based storage and class-based policies. The model is validated using a real warehouse dataset and tested against uncertainties in customer demand and for different travel time models.

就资本和劳动力而言，订单拣选是许多仓库中最苛刻的活动之一。在零件到拣选系统中，自动车辆或起重机将零件运送到人工拣选机。仓库分配策略（产品到仓库位置的分配）会影响订单拣选效率。常用的存储分配策略（例如基于全周转和基于类的存储）仅考虑请求每种产品的频率，而忽略有关联合订购产品的频率的信息，即产品亲和力。仓库可以使用产品的亲和力，以做出明智的决定和分配多个相关产品，以同样的库存“吊舱减少检索时间。现有的基于亲和力的分配按顺序对具有高亲和力的产品进行聚类，然后将聚类分配给存储位置。我们基于产品周转率和从历史客户订单获得的亲和力，提出了一种集成集群分配（ICA）策略，以最大程度地缩短零件选择器系统的检索时间。我们制定了一个数学模型，可以解决小实例，并开发出贪婪的构造启发法来解决大实例。与基于完全周转的存储和基于类的策略相比，ICA存储策略可以将总检索时间减少多达40％。该模型使用真实的仓库数据集进行了验证，并针对客户需求的不确定性和不同的旅行时间模型进行了测试。