An Intelligent and Automated Approach for Smart Minimarkets

Authors

Talal A. Edwan^{1, *,} , Ashraf Tahat^2, , Sara Hammouri¹, Leen Hashem^1, , Leen Da'boul¹

¹Department of Computer Engineering, Princess Sumaya University for Technology, Amman, Jordan

²Department of Communications Engineering, Princess Sumaya University for Technology, Amman, Jordan

^*Corresponding author. Email: t.edwan@psut.edu.jo

DOI

10.2991/ijcis.d.200611.001How to use a DOI?

Keywords

Smart cities; ANN; Smart minimarket; Smart shopping; Queueing analysis

Abstract

This paper presents the design and implementation of a smart and safe minimarket prototype for deployment in busy smart cities to mitigate the overhead of shopping experience. The prototype allows customers to remotely access and browse the available products at the minimarket using a special smart-phone application. The system can intelligently detect the nearby location of customers and subsequently provide location-dependent services such as allowing orders to be placed using the application, predicting weekly customer expenditures based on artificial-neural-network machine-learning approach, and automatically delivering purchased products using a robotic shopping cart. This proposal is believed to support safe shopping which became a critical issue after COVID-19 pandemic. From a service provider view point, the application allows the provider to remotely manage the minimarket by adding/removing product items, keeping track of shortage in products, and getting revenue information. Empirical results show that the average service time of the minimarket is ≈60 seconds per customer. However, an analytical model based on queueing theory was used to analyze the performance of the system when customers arrive according to a Poisson random process and get served according to a general-service-time distribution (M/G/1). The case of batch customer arrivals (M^[H]/G/1) was also analyzed, where batch size is also assumed to be random. Various traffic intensities and the effect of variable service times were studied and cross-validated with simulation results. Worst-case scenario shows that under heavy load of 95%, when customers arrive at the minimarket every 63 seconds on average, the average response time for each customer is ≈10 minutes.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Talal A. Edwan
AU  - Ashraf Tahat
AU  - Sara Hammouri
AU  - Leen Hashem
AU  - Leen Da'boul
PY  - 2020
DA  - 2020/06/23
TI  - An Intelligent and Automated Approach for Smart Minimarkets
JO  - International Journal of Computational Intelligence Systems
SP  - 852
EP  - 863
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200611.001
DO  - 10.2991/ijcis.d.200611.001
ID  - Edwan2020
ER  -