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Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.


Papers
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Journal ArticleDOI
TL;DR: This paper describes an integrated framework for inventory management and pricing in a discrete time (periodic review and ordering) framework, and describes an efficient algorithm, including a new approximation, for the related optimization problem.
Abstract: In price-sensitive markets, price promotions coupled with an appropriate item replenishment strategy can be effective in controlling the total costs of servicing the market. In supply chains that handle perishable products, inventory management is already a complex problem and the management of products in a dynamic-pricing environment is even more challenging. Monitoring and control of time-sensitive products can be facilitated by the application of radio frequency identification (RFID) technology, which enables non-contact, real-time data collection and efficient interfacing with the management control system in the supply chain. This paper describes an integrated framework for inventory management and pricing in a discrete time (periodic review and ordering) framework, and describes an efficient algorithm, including a new approximation, for the related optimization problem. We then propose a suitable architecture for the application of RFID technology in this context, to realize the potential benefits.

61 citations

Journal ArticleDOI
TL;DR: This work considers a third dimension---in addition to time and inventory level---that the firms can use in setting their prices: the information that the firm has at the individual customer level, and shows that the optimal pricing policy is of threshold-type and that the threshold is monotonic in the inventory level and time.
Abstract: Prior work has investigated time-and inventory-level-dependent pricing of limited inventories with finite selling horizons. We consider a third dimension---in addition to time and inventory level---that the firms can use in setting their prices: the information that the firm has at the individual customer level. An arriving customer provides a signal to the firm, which is an imperfect indicator of the customer's willingness to pay, and the firm makes a personalized price offer depending on the signal, inventory level, and time. We consider two different models: full personalization and partial personalization. In the full personalization model, the firm charges any price it wishes given the customer signal, while in the partial personalization model, the firm can charge one of two prices. We find that a mere correlation between the signals and customers' willingness to pay is not sufficient to ensure intuitive relationships between the signal and the optimal prices. We determine a stronger condition, which leads to several structural properties, including the monotonicity of the optimal price with respect to the signal in the full personalization model. For the partial personalization model, we show that the optimal pricing policy is of threshold-type and that the threshold is monotonic in the inventory level and time.

61 citations

Journal ArticleDOI
TL;DR: This paper proposes a two-tier pricing game theoretic framework with two models: static and dynamic pricing models and designs an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femtocell operators.
Abstract: Cognitive femtocell has been envisioned as a promis- ing technology for covering indoor environment and assisting heavy-loaded macrocell network. Although lots of technical issues of cognitive femtocell network have been studied, e.g., spectrum sharing, interference mitigation, etc., the economic issues that are very important for practical femtocell deployment have not been well investigated in the literatures. In this paper, we focus on the pricing issues in the cognitive femtocell network and propose a two-tier pricing game theoretic framework with two models: static and dynamic pricing models. In the static pricing model, we derive the closed-form expressions for pricing and demand functions, as well as the Nash equilibrium pricing strategies for both macrocell and femtocell operators. In the dynamic pricing model, we first model the cognitive users' network access behavior as a two-dimensional Markov decision process and propose a modified value iteration algorithm to find the best strategy profiles for cognitive users. Based on the analysis of users' behavior, we further design an iterative gradient descent algorithm to find the Nash equilibrium pricing strategies for both macrocell and femto- cell operators. Simulation results verify our theoretic analysis and show that the proposed algorithm in the dynamic pricing model can quickly converge to the Nash equilibrium prices.

61 citations

Journal ArticleDOI
TL;DR: In this article, the authors identify the pricing strategy of three most commonly used on-demand food service platforms in China based on detailed information of more than 240,000 orders, and further evaluate the impact of pricing strategy on platform performance.

61 citations

Journal ArticleDOI
27 May 2016-Science
TL;DR: In a recent study, Cramer and Krueger show that ride-sharing has dramatically increased the usage of drivers and their cars, cutting costs for riders and highlighting the opportunities provided by digital markets.
Abstract: Recent advances in information technology are enabling new markets and revolutionizing many existing markets. For example, taxicabs used to find passengers through chance drive-bys or slow central dispatching (see the photo). Location tracking, computer navigation, and dynamic pricing now enable ride-sharing services such as Uber to offer low and consistent delay times of only a few minutes. In a recent study, Cramer and Krueger ( 1 ) show that ride-sharing has dramatically increased the usage of drivers and their cars, cutting costs for riders. The results highlight the opportunities provided by digital markets. Further efficiency gains may come from academia-industry collaborations, which could also help to ensure that the markets develop in ways that further the public interest.

61 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023140
2022262
2021307
2020324
2019346
2018314