A class of random algorithms for inventory cycle offsetting Online publication date: Tue, 29-Jul-2014
by Ernest Croot; Kai Huang
International Journal of Operational Research (IJOR), Vol. 18, No. 2, 2013
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Operational Research (IJOR):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook