An IoT Application for Inventory Management with a Self-Adaptive Decision Model

Lizong Zhang; Nawaf Alharbe; Anthony S. Atkins

doi:10.1109/iThings-GreenCom-CPSCom-SmartData.2016.77

Back

Conference proceeding

An IoT Application for Inventory Management with a Self-Adaptive Decision Model

Lizong Zhang, Nawaf Alharbe and Anthony S. Atkins

2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp.317-322

12/2016

DOI: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.77

Abstract

Companies

Decision making

Distributed Decision Support

Inventory management

Knowledge based systems

Merging

Radiofrequency identification

RFID

Self-adaptive System

Safety storage in large warehouse is an urgent issue to be addressed by both the local authorities and businesses, especially after the Tianjin explosions. This paper proposes an inventory management system for a warehousing company. The system integrates RFID technology and a self-Adaptive distributed decision support model for inbound and outbound actives, inventory location suggestions and incident handling. The model consists of three major components: environment recognition, knowledge merging and the decision making. In addition, a 'self-adaptive' feature is adopted for adjusting the knowledge used in decision making procedure. An experiment is also outlined to validate the utilisations of our model and the proposed system.

Metrics

1 Record Views

Details

Title: An IoT Application for Inventory Management with a Self-Adaptive Decision Model
Creators - without role: Lizong Zhang - University of Electronic Science and Technology of China
Nawaf Alharbe - Taibah University
Anthony S. Atkins - Staffordshire University
Publication Details: 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp.317-322
Publisher: IEEE
Identifiers: 9929534808331
Academic Unit: Taibah University
Language: English
Resource Type: Conference proceeding