Algorithms and Models for Automated Replenishment of Store Shelves – Exploratory Research

Abhinav Majumder, Shiyu Sun, Vittaldas Prabhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Today’s retail store is a hotbed of constant improvement and innovation to provide a seamlessly smooth experience to the customer. With the permeating use of AI/ML and IoT technology, retail chains are moving rapidly to deploy such innovations to their retail stores to improve physical process efficiency and make the customer experience as smooth as possible. Major retailers like Walmart and Ahold Delhaize are experimenting with using robots to automate repetitive tasks like shelf replenishment. There is little research on the issue of automating the replenishment of merchandise inside retail stores during periods of heavy demand for a particular product. In such cases, employees are usually seen scrambling to refill shelves during store operations, thus interfering with customer experience and decreasing physical process efficiency in general. Our goal is to study an approach to automate the process of replenishing products dynamically by leveraging real time data using shelf-stocking robots. Specifically, we study algorithms that dynamically route shelf-stocking robots while minimizing interference with shoppers. The routing algorithms proposed would incorporate congestion awareness to respond to shopper dynamics, thus avoiding interference with shoppers. One key challenge is to model shopper behavior which can be expected to influence the performance of such algorithms. This exploratory research has the potential to influence the way store fronts of the future are designed, enabled by a new class of algorithms for material handling that improves service levels in nextgen storefronts.

Original languageEnglish (US)
Title of host publicationAdvances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings
EditorsErlend Alfnes, Anita Romsdal, Jan Ola Strandhagen, Gregor von Cieminski, David Romero
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Print)9783031436697
StatePublished - 2023
EventIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023 - Trondheim, Norway
Duration: Sep 17 2023Sep 21 2023

Publication series

NameIFIP Advances in Information and Communication Technology
Volume691 AICT
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X


ConferenceIFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023

All Science Journal Classification (ASJC) codes

  • Information Systems and Management

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