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