An association-based clustering approach to order batching considering customer demand patterns

An order-clustering model based on 0-1 integer programming can be formulated to maximize the associations between orders within each batch to find quality solutions of order batching problems.

Abstract

Research on warehousing systems has gained interest since the 1980s, reflecting the fact that supply chain management has pursued a demand-driven organization with high product variety, small order sizes, and reliable short response times throughout the supply chain. This market trend has affected warehouse management and operations tremendously. Order batching in a warehouse attempts to achieve high-volume order processing operations by consolidating small orders into batches. Order batching is an essential operation of order processing in which several orders are grouped into batches. This paper describes the development of an order batching approach based on data mining and integer programming. It is valuable to discover the important associations between orders such that the occurrence of some orders in a batch will cause the occurrence of other orders in the same batch. An order-clustering model based on 0–1 integer programming can be formulated to maximize the associations between orders within each batch. From the results of several test problems, the proposed approach shows its ability to find quality solutions of order batching problems.