Machine Learning-Assisted Dynamic Proximity-Driven Sorting Algorithm for Supermarket Navigation Optimization - A Simulation-Based Validation

Aug 4, 2026

In-store grocery shopping remains widely preferred despite the growth of online grocery services. Hardware-based navigation systems and shopping-list applications aim to reduce inefficiencies, yet cost-effectiveness, optimization capability, and scalability are still limited. This study proposes a proximity-driven dynamic sorting algorithm assisted by machine learning to optimize grocery shopping. It evaluates two ML-DProSA variants—agglomerative hierarchical clustering and affinity propagation clustering—using a simulation patterned on a real supermarket. Shopping-item sorting leverages individual shopping patterns and timestamp-based item proximity, reducing travel distance. Results show both algorithms improve well times versus unsorted lists and support mobile application development.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Abstract

Introduction

Proposed Method: ML-DProSA

ML Model Variants

Simulation Setup

Performance Evaluation

Results and Findings

Conclusion and Future Work

University of Southern Denmark Machine Learning-Assisted Dynamic Proximity-Driven Sorting Algorithm for Supermarket Navigation Optimization A Simulation-Based Validation Abella, Vincent; Initan, Johnfil; Perez, Jake Mark; Astillo, Philip Virgil; Luis Gerardo Cañete, Jr; Choudhary, Gaurav Published in: Future Internet DOI: 10.3390/fi16080277 Publication date: 2024 Document version: Final published version Document license: CC BY Citation for pulished version (APA): Abella, V. , Initan, J. , Perez, J. M. , Astillo, P. V. , Luis Gerardo Cañete, J. , & Choudhary, G. (2024) . Machine Learning-Assisted Dynamic Proximity-Driven Sorting Algorithm for Supermarket Navigation Optimization: A Simulation-Based Validation. Future Internet, 16(8), Article 277. [https://doi.org/10.3390/fi16080277](https://doi.org/10.3390/fi16080277) Go to publication entry in University of Southern Denmark's Research Portal Terms of use This work is brought to you by the University of Southern Denmark. Unless otherwise specified it has been shared according to the terms for self-archiving. If no other license is stated, these terms apply: • You may download this work for personal use only. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying this open access version If you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk) Download date: 03. Aug. 2026 future internet Article Machine Learning-Assisted Dynamic Proximity-Driven Sorting Algorithm for Supermarket Navigation Optimization: A Simulation-Based Validation Vincent Abella 1, Johnfil Initan 1, Jake Mark Perez 1, Philip Virgil Astillo 1, Luis Gerardo Cañete, Jr. 1 and Gaurav Choudhary 2, * Citation: Abella, V.; Initan, J.; Perez, J.M.; Astillo, P.V.; Cañete, L.G., Jr.; Choudhary, G. Machine Learning-Assisted Dynamic Proximity-Driven Sorting Algorithm for Supermarket Navigation Optimization: A Simulation-Based Validation. Future Internet 2024, 16, 277. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)fi16080277 Academic Editor: Andrey V. Savkin Received: 9 June 2024 Revised: 26 July 2024 Accepted: 31 July 2024 Published: 2 August 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Department of Computer Engineering, University of San Carlos, Cebu 6000, Philippines; [18101371@usc.edu.ph](18101371@usc.edu.ph) (V.A.); [20102663@usc.edu.ph](20102663@usc.edu.ph) (J.I.); [18104001@usc.edu.ph](18104001@usc.edu.ph) (J.M.P.); [pvbastillo@usc.edu.ph](pvbastillo@usc.edu.ph) (P.V.A.); [lscanete@usc.edu.ph](lscanete@usc.edu.ph) (L.G.C.J.) 2 Center for Industrial Software, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 6400 Sonderborg, Denmark * Correspondence: [gac@mmmi.sdu.dk](gac@mmmi.sdu.dk) Abstract: In-store grocery shopping is still widely preferred by consumers despite the rising popularity of online grocery shopping. Moreover, hardware-based in-store navigation systems and shopping list applications such as Walmart’s Store Map, Kroger’s Kroger Edge, and Amazon Go have been developed by supermarkets to address the inefficiencies in shopping. But even so, the current systems’cost-effectiveness, optimization capability, and scalability are still an issue. In order to address the existing problems, this study investigates the optimization of grocery shopping by proposing a proximity-driven dynamic sorting algorithm with the assistance

FAQs

Why is an optimization approach needed for in-store grocery shopping?

In-store shopping is still preferred, but existing navigation and shopping-list systems face limitations in cost-effectiveness, optimization capability, and scalability.

What does the proposed ML-DProSA algorithm do?

ML-DProSA uses unique shopper shopping patterns and timestamp-based proximity of items to dynamically sort grocery lists and reduce traveled distance.

Which machine learning models are compared in the simulation study?

The study evaluates two ML-DProSA variants: agglomerative hierarchical clustering and affinity propagation clustering under different setups and configurations.

Aug 4, 2026

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