Abstract
Multiple-criteria ABC (MCABC) analysis is conducted using a dominance-based rough set approach. ABC analysis, a well-known technique for inventory planning and control, divides stock-keeping units (SKUs) into three classes according to annual dollar usage. But MCABC analysis offers more managerial flexibility by including other criteria, such as lead time and criticality, in the classification of SKUs. The objective of this paper is to propose an MCABC method that uses the dominance-based rough set approach to generate linguistic rules that represent a decision-maker’s preferences based on the classification of a test data set. These linguistic rules are then used to classify all SKUs. A case study demonstrates that the procedure is feasible.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chakravarty, A.K.: Multi-item inventory aggregation into groups. Journal of Operational Research Society 32, 19–26 (1981)
Chen, Y., Li, K.W., Kilgour, D.M., Hipel, K.W.: A Case-based distance model for multiple criteria ABC analysis. Computers and Operations Research (in press, 2006)
Chen, Y., Kilgour, D.M., Hipel, K.W.: Multiple criteria classification with an application in water resources planning. Computers and Operations Research 33, 3301–3323 (2006)
Cohen, M.A., Ernst, R.: Multi-item classification and generic inventory stock control policies. Production and Inventory Management Journal 29, 6–8 (1988)
Doumpos, M., Zopounidis, C.: Multicriteria decision aid classification methods. Kluwer, Dordrecht (2002)
Flores, B.E., Whybark, D.C.: Multiple criteria ABC analysis. International Journal of Operations and Production Management 6, 38–46 (1986)
Flores, B.E., Whybark, D.C.: Implementing multiple criteria ABC analysis. Journal of Operations Management 7, 79–84 (1987)
Flores, B.E., Olson, D.L., Dorai, V.K.: Management of multicriteria inventory classification. Mathematical and Computer Modeling 16, 71–82 (1992)
Greco, S., Matarazzo, B., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129, 1–47 (2001)
Institute of Computing Sceience, Poznan University of Technology, Poland, 4eMka2 software, http://idss.cs.put.poznan.pl/site/4emka.html (accessed on March 18, 2006)
Keeney, R.L., Raiffa, H.: Decision with multiple objectives: preferences and value tradeoffs. Wiley, New York (1976)
Pareto, V.: Mannual of Political Economy (English translation). A.M. Kelley Publishers, New York (1971)
Partovi, F.Y., Anandarajan, M.: Classifying inventory using an artificial neural network approach. Computers and Industrial Engineering 41, 389–404 (2002)
Partovi, F.Y., Hopton, W.E.: The analytic hierarchy process as applied to two types of inventory problems. Production and Inventory Management Journal 35, 13–19 (1994)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Roy, B.: Multicriteria methodology for decision aiding. Kluwer, Dordrecht (1996)
Saaty, T.L.: The Analytic Hierarchy Process. McGraw Hill, New York (1980)
Silver, E.A., Pyke, D.F., Peterson, R.: Inventory management and production planning and scheduling, 3rd edn. Wiley, New York (1998)
Swamidass, P.M.: ABC analysis or ABC classification. In: Swamidass, P.M. (ed.) Encyclopedia of production and manufacturing management, vol. 1-2. Kluwer Academic Publishers, Boston (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Y., Li, K.W., Levy, J., Hipel, K.W., Kilgour, D.M. (2006). Rough-Set Multiple-Criteria ABC Analysis. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_35
Download citation
DOI: https://doi.org/10.1007/11908029_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
Online ISBN: 978-3-540-49842-1
eBook Packages: Computer ScienceComputer Science (R0)