Competitive analysis of online inventory problem with interrelated prices
Competitive analysis of online inventory problem with interrelated prices
复制标题
具有相关价格的在线库存问题的竞争分析
DOI：
10.1007/s11766-017-3360-4
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发表时间：
2017-06
影响因子：
1
通讯作者：
Zhou Di wei
中科院分区：
文献类型：
--
作者：
Han Shu guang;Guo Jiu ling;Zhang Lu ping;Hu Jue liang;Jiang Yi wei;Zhou Di wei
本文研究了具有相关价格的在线库存问题，其中即使没有未来价格的具体知识，也必须以在线方式决定何时和补充多少。本文提出了四种不同价格相关性的在线模型，即线性下降模型、对数下降模型、对数模型和指数模型。针对前两种模型，分别给出了在线算法，并作为在线算法的性能指标，给出了算法竞争比的上、下界。对于指数模型和对数模型，分别给出了线性规划求解的在线算法，并分析了相应的竞争比。另外，针对指数模型设计的算法是最优的，而针对对数模型设计的算法只有在一定的条件下才是最优的。此外，一些数值例子表明，基于价格保守策略的算法是更适合当购买价格波动相对平坦。
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.
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影响因子：
1.1
作者：
El-Yaniv, R;Fiat, A;Turpin, G
通讯作者：
Turpin, G
影响因子：
12
作者：
S. Webster;Z. Weng
通讯作者：
S. Webster;Z. Weng
DOI：
10.1109/icmlc.2004.1382313
发表时间：
2004-08
期刊：
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
影响因子：
--
作者：
Wei-Min Ma;Guo-Qing Chen
通讯作者：
Wei-Min Ma;Guo-Qing Chen
影响因子：
1.7
作者：
Yu-Jen Lin;Chia-Huei Ho
通讯作者：
Yu-Jen Lin;Chia-Huei Ho
DOI：
10.1016/j.cie.2010.08.008
发表时间：
2010-11
期刊：
Comput. Ind. Eng.
影响因子：
--
作者：
Snigdha Banerjee;Ashish Sharma
通讯作者：
Snigdha Banerjee;Ashish Sharma