Matthias Ulrich | Searcharxiv

arXiv2022

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

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.

econ.GN↗

arXiv2022

Dynamic Stochastic Inventory Management in E-Grocery Retailing

E-grocery retailing enables ordering products online to be delivered at a future time slot chosen by the customer. This emerging field of business provides retailers with large and comprehensive new data sets, yet creates several challenges for the inventory management process. For example, the risk of a single item's stock-out leading to a complete cancellation of the shopping process is higher in e-grocery than in traditional store retailing. As a consequence, retailers aim at very high service level targets to provide satisfactory customer service and to ensure long-term business growth. When determining replenishment order quantities, it is of crucial importance to precisely account for the full uncertainty in the inventory process. This requires predictive and prescriptive analytics to (1) estimate suitable underlying probability distributions to represent the uncertainty caused by non-stationary customer demand, shelf lives, and supply, and to (2) integrate those forecasts into a comprehensive multi-period optimisation framework. In this paper, we model this stochastic dynamic problem by a sequential decision process that allows us to avoid simplifying assumptions commonly made in the literature, such as the focus on a single demand period. As the resulting problem will typically be analytically intractable, we propose a stochastic lookahead policy incorporating Monte Carlo techniques to fully propagate the associated uncertainties in order to derive replenishment order quantities. This policy naturally integrates probabilistic forecasts and allows us to explicitly derive the value of accounting for probabilistic information compared to myopic or deterministic approaches in a simulation-based setting. In addition, we evaluate our policy in a case study based on real-world data where underlying probability distributions are estimated from historical data and explanatory variables.

econ.GN↗