How to benefit from order data: correlated dispersed storage assignment in robotic warehouses

In e-commerce fulfilment centres, storage assignment is critical to ensure short response times. To achieve this, many online retailers have moved to product dispersion in combination with product turnover-based slotting. However, commonly used policies do not fully utilise the historical customer demand information to optimise the storage assignment. This paper addresses a comprehensive approach to estimate the joint effects of 'turnover frequency', 'product correlation', and 'inventory dispersion' storage strategies on the expected order picking travel time in automated (robotic), parts-to-picker systems. Additionally, it provides a thorough analysis of the impact of product correlation and turnover frequency on storage policies' performance. We develop a mixed-integer linear program for optimal product-to-cluster and cluster-to-zone allocation to minimise the robot's expected travel time. The travel time expressions are developed for different zone and station configurations. An efficient construction and improvement heuristic method is proposed and applied to a real dataset of a personal care products distributor. The analytical results show that the correlated dispersed assignment leads to a shorter expected travel time than the benchmark policies for order sets with sufficiently large order size. The demand correlation plays a major role in the performance of the models in the cases we tested.

Automated summary

This paper focuses on optimizing storage assignment in e-commerce fulfilment centres to improve order picking travel time. The study finds that correlated dispersed assignment leads to a shorter travel time and the impact of product correlation on model performance should be considered.