Topic
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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TL;DR: Arcturus as mentioned in this paper is an international database of dynamic pricing and time-of-use pricing studies that contains the demand response impacts of 163 pricing treatments that were offered on an experimental or full-scale basis in 34 projects in seven countries located in four continents.
Abstract: This paper introduces Arcturus, an international database of dynamic pricing and time-of-use pricing studies. It contains the demand response impacts of 163 pricing treatments that were offered on an experimental or full-scale basis in 34 projects in seven countries located in four continents. The treatments included various types of dynamic pricing rates and simple time-of-use rates, some of which were offered with enabling technologies such as smart thermostats. The demand response impacts of these treatments vary widely, from 0% to more than 50%, and this discrepancy has led some observers to conclude that we still don’t know whether customers respond to dynamic pricing. We find that much of the discrepancy in the results goes away when demand response is expressed as a function of the peak to off-peak price ratio. We then observe that customers respond to rising prices by lowering their peak demand in a fairly consistency fashion across the studies. The response curve is nonlinear and is shaped in the form of an arc: as the price incentive to reduce peak use is raised, customers respond by lowering peak use, but at a decreasing rate. We also find that the use of enabling technologies boosts the amount of demand response. Overall, we find a significant amount of consistency in the experimental results, especially when the results are disaggregated into two categories of rates: time-of-use rates and dynamic pricing rates. This consistency evokes the consistency that was found in earlier analysis of time-of-use pricing studies that was carried out by EPRI in the early 1980s. Our analysis supports the case for the rollout of dynamic pricing wherever advanced metering infrastructure is in place.
55 citations
27 Jun 2011
TL;DR: A novel efficient network management framework to tackle the challenges of the maintenance of the energy supply, the support of the quality-of-information (QoI) requirements, and the generation of maximum revenue for network operator, but with sparsely research exposure is proposed.
Abstract: Participatory sensing is becoming more popular with the help of sensor-embedded smartphones to retrieve context-aware information for users. However, new challenges arise for the maintenance of the energy supply, the support of the quality-of-information (QoI) requirements, and the generation of maximum revenue for network operator, but with sparsely research exposure. This paper proposes a novel efficient network management framework to tackle the above challenges, where four key design elements are introduced. First is the QoI satisfaction index, where the QoI benefit the queries receive is quantified in relation to the level they require. Second is the credit satisfaction index, where the credits are used by the network operator to motivate the user participation, and this index is to quantify its degree of satisfaction. Third is the Gur Game-based distributed energy control, where the above two indexes are used as inputs to the mathematical framework of the Gur Game for distributed decision-making. Fourth is the dynamic pricing scheme, based on a constrained optimization problem to allocate credits to the participants while minimizing the necessary adaptation of the pricing scheme from the network operator. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7% detection outage, saves 80% of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4% gap if compared with optimal solution.
55 citations
TL;DR: A family of pricing heuristics that requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time and provides the seller with extra flexibility in managing his prices.
Abstract: We study a standard dynamic pricing problem where the seller (a monopolist) possesses a finite amount of inventories and attempts to sell the products during a finite selling season. Despite the potential benefits of dynamic pricing, many sellers still adopt a static pricing policy because of (1) the complexity of frequent reoptimizations, (2) the negative perception of excessive price adjustments, and (3) the lack of flexibility caused by existing business constraints. In this paper, we develop a family of pricing heuristics that can be used to address all these challenges. Our heuristic is computationally easy to implement; it requires only a single optimization at the beginning of the selling season and automatically adjusts the prices over time. Moreover, to guarantee a strong revenue performance, the heuristic only needs to adjust the prices of a small number of products and do so infrequently. This property helps the seller focus his effort on the prices of the most important products instead of all products. In addition, in the case where not all products are equally admissible to price adjustment (due to existing business constraints such as contractual agreement, strategic product positioning, etc.), our heuristic can immediately substitute the price adjustment of the original products with the price adjustment of similar products and maintain an equivalent revenue performance. This property provides the seller with extra flexibility in managing his prices.
55 citations
Proceedings Article•
11 Apr 2019TL;DR: In this paper, the authors show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms, and introduce algorithms that achieve state-of-the-art dynamic regret bounds for nonstochastic linear bandit setting.
Abstract: We introduce algorithms that achieve state-of-the-art dynamic regret bounds for non-stationary linear stochastic bandit setting. It captures natural applications such as dynamic pricing and ads allocation in a changing environment. We show how the difficulty posed by the non-stationarity can be overcome by a novel marriage between stochastic and adversarial bandits learning algorithms. Our main contributions are the tuned Sliding Window UCB (SW-UCB) algorithm with optimal dynamic regret, and the tuning free bandit-over-bandit (BOB) framework built on top of the SW-UCB algorithm with best (compared to existing literature) dynamic regret.
55 citations
TL;DR: In this article, the authors study the strategic response of consumers to dynamic prices for perishable products and find that in most cases the policy can benefit both the strategic consumer and the seller.
Abstract: Dynamic pricing is a standard practice that sellers use for revenue management. With the vast availability of pricing and inventory data on the Internet, it is possible for consumers to become aware of the pricing strategies used by sellers and to develop strategic responses. In this chapter, we study the strategic response of consumers to dynamic prices for perishable products. As price fluctuates with the changes in time and inventory, a strategic consumer may choose to postpone a purchase in anticipation of lower prices in the future. We analyze a threshold purchasing policy for the strategic consumer, and conduct numerical studies to study its impact on both the strategic consumer’s benefits and the seller’s revenue. We find that in most cases the policy can benefit both the strategic consumer and the seller. In practice, the seller could encourage consumer waiting by adopting a target price purchasing system.
55 citations