Title: A class of random algorithms for inventory cycle offsetting

Authors: Ernest Croot; Kai Huang

Addresses: Department of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA ' DeGroote School of Business, McMaster University, Hamilton, Ontario L8S 4M4, Canada

Abstract: The inventory cycle offsetting problem (ICP) is a strongly NP-complete problem. We study this problem from the view of probability theory, and rigorously analyse the performance of a specific random algorithm for this problem; furthermore, we present a 'local search' algorithm, and a 'modified local search' algorithm, which give much better results (the 'modified local search' gives better results than plain 'local search'), and lead to good solutions to certain practical instances of ICP, as we demonstrate with some numerical data. The regime where the random algorithm is rigorously proved to work is when the number of items is large, while the time horizon and unit volumes are not too large. Under such natural hypotheses, the law of large numbers, and various quantitative refinements (such as Bernstein's inequality) come into play, and we use these results to show that there always exist good solutions, not merely that good solutions holds with high probability.

Keywords: inventory cycle offsetting; replenishment time; number theory; concentration of measure.

DOI: 10.1504/IJOR.2013.056115

International Journal of Operational Research, 2013 Vol.18 No.2, pp.201 - 217

Available online: 31 Aug 2013 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article