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.

Dynamic Pricing and Lead-Time Quotation for a Multiclass Make-to-Order Queue

Abstract: This paper considers a profit-maximizing make-to-order manufacturer that offers multiple products to a market of price and delay sensitive users, using a model that captures three aspects of particular interest: first, the joint use of dynamic pricing and lead-time quotation controls to manage demand; second, the presence of a dual sourcing mode that can expedite orders at a cost; and third, the interaction of the aforementioned demand controls with the operational decisions of sequencing and expediting that the firm must employ to optimize revenues and satisfy the quoted lead times. Using an approximating diffusion control problem we derive near-optimal dynamic pricing, lead-time quotation, sequencing, and expediting policies that provide structural insights and lead to practically implementable recommendations. A set of numerical results illustrates the value of joint pricing and lead-time control policies.

Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions

Abstract: The beneÞts of dynamic pricing methods have long been known in industries, such as airlines, hotels and electric utilities, where the capacity is Þxed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries as well, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: the increased availability of demand data, the ease of changing prices due to new techologies, and the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations. (Dynamic Pricing; E-commerce; Revenue Management; Inventory)

Effects of price framing on consumers’ perceptions of online dynamic pricing practices

Abstract: The viability of online dynamic pricing, or differential pricing for the same product from the same seller, is still debatable given the contradictory findings reported in both modeling and behavioral price research. This paper examines tactical ways for online merchants to mitigate consumers’ negative reactions when adopting dynamic pricing strategies. In three experiments, we show that using various price-framing tactics, compared to no framing, can induce price-disadvantaged consumers to perceive their ostensibly similar transactions differently relative to their comparative other parties. As the degree of perceived transaction dissimilarity increases, price-disadvantaged consumers’ perceived price fairness, trust, and repurchase intentions are enhanced. We further compare different price framing tactics and demonstrate that they have different effects on consumers across different product price levels, customer segments, and framing formats. The paper concludes with theoretical and managerial implications of the research.

Dynamic Mechanism Design for Online Commerce

Abstract: This paper is a mechanism design study for a monopolist selling multiple identical items to potential buyers arriving over time. Participants in our model are time sensitive, with the same discount factor; potential buyers have unit demand and arrive sequentially according to a renewal process; and valuations are drawn independently from the same regular distribution. Invoking the revelation principle, we restrict our attention to direct dynamic mechanisms taking a sequence of valuations and arrival epochs as input. We define two properties (discreteness and stability), and prove under further distributional assumptions that we may at no cost of generality consider only mechanisms satisfying them. This effectively reduces the mechanism input to a sequence of valuations and leads to formulate the problem as a dynamic program (DP). As this DP is equivalent to a well-known infinite-horizon asset-selling problem, we finally characterize the optimal mechanism as a sequence of posted prices increasing with each sale. Remarkably, this result rationalizes somewhat the frequent restriction to dynamic pricing policies and impatient buyers assumption. Our numerical study indicates that, under various valuation distributions, the benefit of dynamic pricing over a fixed posted price may be small. Besides, posted prices are preferable to online auctions for a large number of items or high interest rate, but in other cases auctions are close to optimal and significantly more robust.

Robust Optimal Energy Management of a Residential Microgrid Under Uncertainties on Demand and Renewable Power Generation

Abstract: Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users’ behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users’ demand uncertainties. To this aim, first, we formulate a min–max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min–max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints’ violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners —This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users’ payment and constraints’ violation rate considering the energy cost saving, the system technical limitations and the users’ comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand.