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.

Revenue Management for a Multiclass Single-Server Queue via a Fluid Model Analysis

Abstract: Motivated by the recent adoption of tactical pricing strategies in manufacturing settings, this paper studies a problem of dynamic pricing for a multiproduct make-to-order system. Specifically, for a multiclass Mn/M/1 queue with controllable arrival rates, general demand curves, and linear holding costs, we study the problem of maximizing the expected revenues minus holding costs by selecting a pair of dynamic pricing and sequencing policies. Using a deterministic and continuous (fluid model) relaxation of this problem, which can be justified asymptotically as the capacity and the potential demand grow large, we show the following: (i) greedy sequencing (i.e., the cμ-rule) is optimal, (ii) the optimal pricing and sequencing decisions decouple in finite time, after which (iii) the system evolution and thus the optimal prices depend only on the total workload. Building on (i)--(iii), we propose a one-dimensional workload relaxation to the fluid pricing problem that is simpler to analyze, and leads to intuitive and implementable pricing heuristics. Numerical results illustrate the near-optimal performance of the fluid heuristics and the benefits from dynamic pricing.

Bid price control and dynamic pricing in clouds

Abstract: The term Cloud Computing represents a paradigm for offering different kind of Web services, which can be dynamically developed, composed and deployed on virtualized infrastructure. This work will extend the concepts known from the revenue management to the specific case of Cloud Computing and propose two models, bid price control and a variant of dynamic pricing, that will compete with the commonly used static pricing. Both models will try to maximize revenues by controlling the availability or price of every offered fare class. The aim is to understand from a Cloud Computing company’s perspective, how decisions about the pricing and the optimal allocation of the given resources for the various Cloud Services can be supported. As expected, simulation results show that an optimally adjusted dynamic pricing model will outperform any pricing model with static prices and will simultaneously contribute to slightly smoother resource utilization in some cases. However, we will see that the adjustment itself is difficult to realize, and if conducted suboptimal, it may also have certain disadvantages compared to static prices. In combination with a reasonable product differentiation, the bid-price method performed very solid and in nearly any case better than the pure static pricing model.

ROD-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-on-Demand Service Using Multi-Source Urban Data

Abstract: Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers’ side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability–to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers’ revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers’ revenue by offering useful guidance.