Integrated storage assignment for an e-grocery fulfilment centre: Accounting for day-of-week demand patterns

TL;DR

This paper presents an integrated MILP model for storage assignment in an e-grocery fulfilment centre, accounting for day-of-week demand patterns to improve efficiency and workload balance across days.

Contribution

It introduces a novel integrated approach for storage assignment that considers product selection, station, and shelf allocation simultaneously, including demand variability over the week.

Findings

Integrated approach outperforms sequential methods in efficiency.

Demand-aware storage assignment balances workload across days.

Model maintains high picking efficiency despite demand variation.

Abstract

In this paper, we deal with a storage assignment problem arising in a fulfilment centre of a major European e-grocery retailer. The centre can be characterised as a hybrid warehouse consisting of a highly efficient and partially automated fast-picking area designed as a pick-and-pass system with multiple stations, and a picker-to-parts area. The storage assignment problem considered in this paper comprises the decisions to select the products to be allocated to the fast-picking area, the assignment of the products to picking stations and the determination of a shelf within the assigned station. The objective is to achieve a high level of picking efficiency while respecting station workload balancing and precedence order constraints. We propose to solve this three-level problem using an integrated MILP model. In computational experiments with real-world data, we show that using the proposed integrated approach yields significantly better results than a sequential approach in which the selection of products to be included in the fast-picking area is solved before assigning station and shelf. Furthermore, we provide an extension to the integrated storage assignment model that explicitly accounts for within-week demand variation. In a set of experiments with day-of-week-dependent demands we show that while a storage assignment that is based on average demand figures tends to exhibit a highly imbalanced workload on certain days of the week, the augmented model yields storage assignments that are well balanced on each day of the week without compromising the quality of the solutions in terms of picking efficiency.