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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.


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Journal ArticleDOI
01 Jun 2019-Energy
TL;DR: This study attempts to solve the sustainable smart grid design problem, where three indispensable sustainability dimensions, economy, environment, and society, are considered simultaneously and efficiently tackles both uncertainties in scenarios and parameters with a total cost lower than other models.

33 citations

Journal ArticleDOI
Kyle Y. Lin1
TL;DR: This work formulate the seller's problem as a stochastic dynamic programming model, and develops an algorithm to compute the optimal policy, and derive tight bounds for the optimal expected revenue, and develop an asymptotically optimal heuristic policy.
Abstract: Consider a sequential dynamic pricing model where a seller sells a given stock to a random number of customers. Arriving one at a time, each customer will purchase one item if the product price is lower than her personal reservation price. The seller's objective is to post a potentially different price for each customer in order to maximize the expected total revenue. We formulate the seller's problem as a stochastic dynamic programming model, and develop an algorithm to compute the optimal policy. We then apply the results from this sequential dynamic pricing model to the case where customers arrive according to a continuous-time point process. In particular, we derive tight bounds for the optimal expected revenue, and develop an asymptotically optimal heuristic policy. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

33 citations

Journal ArticleDOI
TL;DR: In this article , a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices that reflects the preferences of multiple stakeholders is proposed to achieve the optimal operation between different entities in the system, and a case study verifies the effectiveness of the proposed method.
Abstract: The coordinated implementation of demand response technology and dynamic energy prices facilitates the interaction among multiple stakeholders in the smart integrated energy system. To achieve the optimal operation between different entities in the system, this paper proposes a multi-objective optimization model for smart integrated energy system considering demand responses and dynamic prices that reflects the preferences of multiple stakeholders. Based on the tightly coupled characteristics of a multi-energy system, a flexible two-dimensional demand response model with spatio-temporal coupling characteristics is established. By analyzing the characteristics of multi-entities joint pricing, the dynamic energy price formulated with the participation of both supplier and demander is optimized, and the dynamic price control strategy of different stakeholders under different benefit weights is obtained. A case study verifies the effectiveness of the proposed method. According to the interest preferences of different entities, different strategies and operating mechanisms can be derived, which is conducive to improving the economy and reliability of the operation of the smart integrated energy system and promoting the interaction between multiple entities.

33 citations

Proceedings ArticleDOI
22 Apr 2001
TL;DR: An option contract that reduces the risk of quality disruption, if a user has a fixed budget at his disposal, and calculate its price is introduced, and one potential use of this methodology is towards developing a simple admission control mechanism for placing voice calls through an IP network, where the decisions can be taken by edge devices.
Abstract: This paper introduces a framework for answering questions regarding the conditions on the network load that allow a best-effort network like the Internet to support connections of given duration that require a certain quality of service. Such quality of service is expressed in terms of the percentage of time the bandwidth allocated to a connection may drop below a certain level or the maximum allowable delay in placing the call through the network waiting for more favorable loading conditions. The call-acceptance conditions, which depend on the behavior of the system over the lifetime of accepted calls, are thus based on transient models for the congestion (instead of looking at the average behavior) and attempt to exploit the time-scales of the fluctuations of the number of connections competing for bandwidth. Extensions of the model consider the case of dynamic pricing which allows connections that pay more to get larger shares of the bandwidth, and investigate the trade-off between quality of service, the size of the acceptance region, and the charge to be paid by the connection. Under this framework we introduce an option contract that reduces the risk of quality disruption, if a user has a fixed budget at his disposal, and calculate its price. One potential use of this methodology is towards developing a simple admission control mechanism for placing voice calls through an IP network, where the decisions can be taken by edge devices.

33 citations

Posted Content
TL;DR: In this article, a generic time-varying fetching and caching costs are formulated to minimize the aggregate cost across files and time, and a light-weight online solver for the corresponding optimization is employed to find optimal fetch-cache decisions.
Abstract: Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by \emph{caching} them at the edge of the network, close to the end users. The ultimate goal is to shift part of the predictable load on the back-haul links, from on-peak to off-peak periods, contributing to a better overall network performance and service experience. To enable the SBs with efficient \textit{fetch-cache} decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs and file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of the generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, $Q$-learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.

33 citations


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