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COMPETITIVE ANALYSIS OF INTERRELATED PRICE ONLINE INVENTORY PROBLEMS WITH DEMANDS

Published online by Cambridge University Press:  15 May 2017

SHUGUANG HAN*
Affiliation:
Department of Mathematics, Zhejiang Sci-Tech University, Hangzhou 310018, China email dawn1024@zstu.edu.cn email hujlhz@163.com
JUELIANG HU
Affiliation:
Department of Mathematics, Zhejiang Sci-Tech University, Hangzhou 310018, China email dawn1024@zstu.edu.cn email hujlhz@163.com
DIWEI ZHOU
Affiliation:
Department of Mathematical Sciences, Loughborough University, LoughboroughLE11 3TU, UK email d.zhou2@lboro.ac.uk
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Abstract

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This paper investigates interrelated price online inventory problems, in which decisions as to when and how much of a product to replenish must be made in an online fashion to meet some demand even without a concrete knowledge of future prices. The objective of the decision maker is to minimize the total cost while meeting the demands. Two different types of demand are considered carefully, that is, demands which are linearly and exponentially related to price. In this paper, the prices are online, with only the price range variation known in advance, and are interrelated with the preceding price. Two models of price correlation are investigated, namely, an exponential model and a logarithmic model. The corresponding algorithms of the problems are developed, and the competitive ratios of the algorithms are derived as the solutions by use of linear programming.

MSC classification

Type
Research Article
Copyright
© 2017 Australian Mathematical Society 

References

Ali, M. M. and Masinga, L., “A nonlinear optimization model for optimal order quantities with stochastic demand rate and price change”, J. Ind. Manag. Optim. 3(1) (2007) 139154; doi:10.3934/jimo.2007.3.139.CrossRefGoogle Scholar
Araman, V. F. and Caldentey, R., “Dynamic pricing for nonperishable products with demand learning”, Oper. Res. 57 (2009) 11691188; doi:10.1287/opre.1090.0725.Google Scholar
Banerjee, S. and Sharma, A., “Inventory model for seasonal-demand with option to change the market”, Comput. Indust. Engng 59 (2010) 807818; doi:10.1016/j.cie.2010.08.008.Google Scholar
Damaschke, P., Ha, P. H. and Tsigas, P., “Online search with time-varying price bounds”, Algorithmica 55 (2009) 619642; doi:10.1007/s00453-007-9156-9.Google Scholar
Drezner, Z. and Scott, C., “Approximate and exact formulas for the (Q, r) inventory model”, J. Ind. Manag. Optim. 11 (2015) 135144; doi:10.3934/jimo.2015.11.135.Google Scholar
El-Yaniv, R., Fiat, A., Karp, R. M. and Turpin, G., “Optimal search and one-way trading online algorithms”, Algorithmica 30 (2001) 101139; doi:10.1007/s00453-001-0003-0.CrossRefGoogle Scholar
Kalymon, B. A., “Stochastic prices in a single-item inventory purchasing model”, Oper. Res. 19 (1971) 14341458; doi:10.1287/opre.19.6.1434.Google Scholar
Larsen, K. S. and Wohlk, S., “Competitive analysis of the online inventory problem”, Eur. J. Oper. Res. 207 (2010) 685696; doi:10.1016/j.ejor.2010.05.019.Google Scholar
Lin, Y. J. and Ho, C. H., “Integrated inventory model with quantity discount and price-sensitive demand”, Top 19 (2011) 177188; doi:10.1007/s11750-009-0132-1.CrossRefGoogle Scholar
Liu, W., Song, S. and Wu, C., “Single-period inventory model with discrete stochastic demand based on prospect theory”, J. Ind. Manag. Optim. 8 (2012) 577590; doi:10.3934/jimo.2012.8.577.CrossRefGoogle Scholar
Sana, S. S., “Price-sensitive demand for perishable items-an EOQ model”, Appl. Math. Comput. 217 (2011) 62486259; doi:10.1016/j.amc.2010.12.113.Google Scholar
Schmidt, G., Mohr, E. and Kersch, M., “Experimental analysis of an online trading algorithm”, Electron. Notes Discrete Math. 36 (2010) 519526; doi:10.1016/j.endm.2010.05.066.Google Scholar
Serel, D. A., “Optimal ordering and pricing in a quick response system”, Int. J. Production Econom. 121 (2009) 700714; doi:10.1016/j.ijpe.2009.04.020.CrossRefGoogle Scholar
Shu, J., Li, Z. Y. and Zhong, W. J., “A market selection and inventory ordering problem under demand uncertainty”, J. Ind. Manag. Optim. 7 (2011) 425434; doi:10.3934/jimo.2011.7.425.Google Scholar
Sicilia, J., González-De-La-Rosa, M., Febles-Acosta, J. and Alcaide-López-De-Pablo, D., “An inventory model for deteriorating items with shortages and time-varying demand”, Int. J. Production Econom. 155 (2014) 155162; doi:10.1016/j.ijpe.2014.01.024.CrossRefGoogle Scholar
Wang, Z., Deng, S. and Ye, Y., “Close the gaps: a learning-while-doing algorithm for single-product revenue management problems”, Oper. Res. 62 (2014) 318331; doi:10.1287/opre.2013.1245.Google Scholar
Webster, S. and Weng, Z. K., “Ordering and pricing policies in a manufacturing and distribution supply chain for fashion products”, Int. J. Production Econom. 114 (2008) 476486; doi:10.1016/j.ijpe.2007.06.010.Google Scholar
Wilson, R. H., “A scientific routine for stock control”, Harvard Business Rev. 13 (1934) 116129.Google Scholar
Xu, Y., Zhang, W. and Zheng, F., “Optimal algorithms for the online time series search problem”, Theor. Comput. Sci. 412 (2009) 192197; doi:10.1016/j.tcs.2009.09.026.CrossRefGoogle Scholar
Yang, P. C., Wee, H. M., Chung, S. L. and Huang, Y. Y., “Pricing and replenishment strategy for a multi-market deteriorating product with time-varying and price-sensitive demand”, J. Ind. Manag. Optim. 9 (2013) 769787; doi:10.3934/jimo.2013.9.769.CrossRefGoogle Scholar
Zhang, W., Xu, Y., Zheng, F. and Dong, Y., “Optimal algorithms for online time series search and one-way trading with interrelated prices”, J. Comb. Optim. 23 (2012) 159166; doi:10.1007/s10878-010-9344-4.Google Scholar