@inproceedings{b3404c7d57dd4c98baba8c76b965c134,
title = "Optimizing inventory grouping decisions: A grouping particle swarm optimization approach",
abstract = "Inventory classification involving thousands of different items is of common occurrence in moderate to large scale organizations. Though widely applied in several industries, the classical ABC inventory analysis has limitations, including inability to handle qualitative criteria, inability to model multiple criteria, and sub-optimal solutions. This research presents an extension to the inventory classification problem. The proposed approach incorporates a multi-criteria grouping perspective based on a particle swarm optimization approach. First, we analyze the grouping structure of the inventory classification problem. Second, we model the problem from a multi-criteria perspective. Third, we present a particle grouping particle swarm optimization approach for the problem. The proposed multi-criteria inventory classification approach is promising. Finally, further research prospects are presented.",
keywords = "ABC analysis, Grouping algorithm, Inventory grouping, Multi-criteria optimization, Particle Swarm Optimization",
author = "Michael Mutingi and Harmony Musiyarira and Charles Mbohwa and Partson Dube",
note = "Publisher Copyright: {\textcopyright} Copyright International Association of Engineers.; 2017 World Congress on Engineering and Computer Science, WCECS 2017 ; Conference date: 25-10-2017 Through 27-10-2017",
year = "2017",
language = "English",
isbn = "9789881404756",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "468--471",
editor = "Ao, {S. I.} and Grundfest, {W. S.} and Craig Douglas",
booktitle = "Proceedings of the World Congress on Engineering and Computer Science 2017, WCECS 2017",
}