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: A new model for optimal dynamic pricing of perishable services or products that incorporates a simple risk measure permitting control of the probability that total revenues fall below a minimum acceptable level is presented.
Abstract: We present a new model for optimal dynamic pricing of perishable services or products that incorporates a simple risk measure permitting control of the probability that total revenues fall below a minimum acceptable level. The formulation assumes that sales must occur within a finite time period, that there is a finite---possibly large---set of available prices, and that demand follows a price-dependent, nonhomogeneous Poisson process. This model is particularly appropriate for applications in which attainment of a revenue target is an important consideration for managers; for example, in event management, in seasonal clearance of high-value items, or for business subunits operating under performance targets. We formulate the model as a continuous-time optimal control problem, obtain optimality conditions, explore structural properties of the solution, and report numerical results on problems of realistic size.
91 citations
TL;DR: This paper discusses the formulation of crowding in public transport and its implications for pricing, seating capacity and optimal scheduling, and derives the optimal dynamic pricing and optimal share of seats for the one OD pair case.
Abstract: This paper discusses the formulation of crowding in public transport and its implications for pricing, seating capacity and optimal scheduling. An analytical model is used to describe the user equilibrium and the optimal equilibrium for different stylized conditions. For the one OD pair case with identical desired arrival time, we derive the optimal dynamic pricing and optimal share of seats. For the uniformly distributed desired arrival times case, we derive the optimal time table and the optimal pricing. Next we generalize the results to the case of a small network with several stations, stochastic choice and allocation of seats.
91 citations
TL;DR: The authors analyzes the impact of hotel price sequences on consumers' reference prices through a lab and a field experiment, showing that consumers decrease their reference price when competing hotels adjust their prices simultaneously.
91 citations
15 Dec 2011
TL;DR: This paper proposes a system, developed within the BEE Project, for predicting the usage of household appliances in order to automatically provide inputs to electricity management mechanism, exactly in the same way a user could do.
Abstract: Electricity demand management mechanisms are expected to play a key role in smart grid infrastructures to reduce buildings power demand at peak hours, by means of dynamic pricing strategies. Unfortunately these kinds of mechanisms require the users to manually set a lot of configuration parameters, thereby reducing the usability of these solutions. In this paper we propose a system, developed within the BEE Project, for predicting the usage of household appliances in order to automatically provide inputs to electricity management mechanism, exactly in the same way a user could do. In our architecture we use a wireless power meter sensor network to monitor home appliances consumption. Data provided by sensors are then processed every 24 hours to forecast which devices will be used on the next day, at what time and for how long. This information represents just the input parameters required by load demand management systems, hence avoiding complex manual settings by the user.
90 citations
TL;DR: An intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PH EVs users is proposed.
Abstract: Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture—an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides real-time dynamic price as home-price and roaming-price , depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered—home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs’ mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately $34$ percent over that of the existing ones for optimal charging of PHEVs.
90 citations