How to Benefit from Order Data: Correlated Dispersed Storage Assignment in Robotic Warehouses

Masoud Mirzaei*, N Zaerpour, Rene de Koster

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
5 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)549-568
Number of pages20
JournalInternational Journal of Production Research
VolumeTo appear
Issue number2
DOIs
Publication statusPublished - 13 Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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