Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information

2014 ◽  
Vol 24 (5) ◽  
pp. 704-731 ◽  
Author(s):  
Ming Chen ◽  
Zhi-Long Chen
2020 ◽  
Author(s):  
Will Ma ◽  
David Simchi-Levi ◽  
Chung-Piaw Teo

Dynamic Pricing with Limited Demand Information


Author(s):  
Rainer Schlosser ◽  
Carsten Walther ◽  
Martin Boissier ◽  
Matthias Uflacker

Online markets are characterized by competition and limited demand information. In E-commerce, firms compete against each other using data-driven dynamic pricing and ordering strategies. To successfully manage both inventory levels as well as offer prices is a highly challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. Currently, retailers lack the possibility to test and evaluate their algorithms appropriately before releasing them into the real world. To study joint dynamic ordering and pricing competition on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics. Further, we deployed and compared different pricing and ordering strategies, from simple rule-based ones to highly sophisticated data-driven strategies which are based on state-of-the-art demand learning techniques and efficient dynamic optimization models.


2008 ◽  
Vol 54 (9) ◽  
pp. 1594-1609 ◽  
Author(s):  
Yingjie Lan ◽  
Huina Gao ◽  
Michael O. Ball ◽  
Itir Karaesmen

2010 ◽  
Vol 9 (1-2) ◽  
pp. 23-48 ◽  
Author(s):  
Serkan S Eren ◽  
Costis Maglaras

2021 ◽  
Vol 13 (19) ◽  
pp. 10746
Author(s):  
Ying Gao ◽  
Jianteng Xu ◽  
Huixin Xu

Carbon emission reduction is increasingly becoming a public consensus, with governments formulating carbon emission policies, enterprises investing in emission abatement equipment, and consumers having a low-carbon preference. On the other hand, it is difficult for industry managers to obtain all the demand information. Based on this, this paper aims to investigate operations and coordination for a sustainable system with a flexible cap-and-trade policy and limited demand information. Newsvendor and distribution-free newsvendor models are formulated to show the validity of limited information. Stackelberg game is exploited to derive optimal abatement and order quantity solutions under centralized and decentralized systems. The revenue-sharing and two-part tariff contracts are then proposed to coordinate the decentralized system with limited demand information. Numerical analyses complement the theoretical results. We list some major findings. Firstly, we discover that using abatement equipment can effectively reduce emissions and increase profits. Secondly, the distribution-free approach is effective and acceptable for a system where only mean and variance information is informed. Thirdly, the mean parameter has a greater impact on profits and emissions comparing with the other seven parameters. Finally, we show that both contracts may achieve perfect coordination, and the two-part tariff contract is more robust.


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