Competitive analysis of online inventory problem with interrelated prices
journal contributionposted on 21.07.2017 by Shuguang Han, Jiuling Guo, Luping Zhang, Jueliang Hu, Yiwei Jiang, Diwei Zhou
Any type of content formally published in an academic journal, usually following a peer-review process.
This paper investigates the online inventory problem with interrelated prices in which a decision of when and how much to replenish must be made in an online fashion even without concrete knowledge of future prices. Four new online models with different price correlations are proposed in this paper, which are the linear-decrease model, the log-decrease model, the logarithmic model and the exponential model. For the first two models, the online algorithms are developed, and as the performance measure of online algorithm, the upper and lower bounds of competitive ratios of the algorithms are derived respectively. For the exponential and logarithmic models, the online algorithms are proposed by the solution of linear programming and the corresponding competitive ratios are analyzed, respectively. Additionally, the algorithm designed for the exponential model is optimal, and the algorithm for the logarithmic model is optimal only under some certain conditions. Moreover, some numerical examples illustrate that the algorithms based on the dprice-conservative strategy are more suitable when the purchase price fluctuates relatively flat.
Supported by the National Natural Science Foundation of China (11571013, 11471286).
- Mathematical Sciences