A classification approach based on the outranking model for multiple criteria ABC analysis

Jiapeng Liu, Xiuwu Liao, Wenhong Zhao and Na Yang

Omega, 2016, vol. 61, issue C, 19-34

Abstract: The multiple criteria ABC analysis is widely used in inventory management, and it can help organizations to assign inventory items into different classes with respect to several evaluation criteria. Many approaches have been proposed in the literature for addressing such a problem. However, most of these approaches are fully compensatory in multiple criteria aggregation. This means that an item scoring badly on one or more key criteria could be placed in good classes because these bad performances could be compensated by other criteria. Thus, it is necessary to consider the non-compensation in the multiple criteria ABC analysis. To the best of our knowledge, the ABC classification problem with non-compensation among criteria has not been studied sufficiently. We thus propose a new classification approach based on the outranking model to cope with such a problem in this paper. However, the relational nature of the outranking model makes the search for the optimal classification solution a complex combinatorial optimization problem. It is very time-consuming to solve such a problem using mathematical programming techniques when the inventory size is large. Therefore, we combine the clustering analysis and the simulated annealing algorithm to search for the optimal classification. The clustering analysis groups similar inventory items together and builds up the hierarchy of clusters of items. The simulated annealing algorithm searches for the optimal classification on different levels of the hierarchy. The proposed approach is illustrated by a practical example from a Chinese manufacturer. Furthermore, we validate the performance of the approach through experimental investigation on a large set of artificially generated data at the end of the paper.

Keywords: ABC inventory classification; Multiple criteria decision analysis; Clustering; Simulated annealing algorithm (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0305048315001462
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:61:y:2016:i:c:p:19-34

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.omega.2015.07.004

Access Statistics for this article

Omega is currently edited by B. Lev

More articles in Omega from Elsevier
Bibliographic data for series maintained by Catherine Liu ().