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.

Personalized Dynamic Pricing with Machine Learning: High-Dimensional Features and Heterogeneous Elasticity

Abstract: We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature...

Dynamic monopoly pricing and herding

Abstract: This paper studies dynamic pricing by a monopolist selling to buyers who learn from each other’s purchases. The price posted in each period serves to extract rent from the current buyer, as well as to control the amount of information transmitted to future buyers. As information increases future rent extraction, the monopolist has an incentive to subsidize learning by charging a price that results in information revelation. Nonetheless in the long run, the monopolist generally induces herding by either selling to all buyers or exiting the market.

Sourcing from Multiple Suppliers for Price‐Dependent Demands

Abstract: We analyze a model that integrates demand shaping via dynamic pricing and risk mitigation via supply diversification. The firm under consideration replenishes a certain product from a set of capacitated suppliers for a price-dependent demand in each period. Under deterministic capacities, we derive a multilevel base stock list price policy and establish the optimality of cost-based supplier selection, that is, ordering from a cheaper source before more expensive ones. With general random capacities, however, neither result holds. While it is optimal to price low for a high inventory level, the optimal order quantities are not monotone with respect to the inventory level. In general, a near reorder-point policy should be followed. Specifically, there is a reorder point for each supplier such that no order is issued to him when the inventory level is above this point and a positive order is placed almost everywhere when the inventory level is below this point. Under this policy, it may be profitable to order exclusively from the most expensive source. We characterize conditions under which a strict reorder-point policy and a cost-based supplier-selection criterion become optimal. Moreover, we quantify the benefit from dynamic pricing, as opposed to static pricing, and the benefit from multiple sourcing, as opposed to single sourcing. We show that these two strategies exhibit a substitutable relationship. Dynamic pricing is less effective under multiple sourcing than under single sourcing, and supplier diversification is less valuable with price adjustments than without. Under limited supply, dynamic pricing yields a robust, long-term profit improvement. The value of supply diversification, in contrast, mainly comes from added capacities and is most significant in the short run.

When Blockchain Meets Smart Grid: Secure Energy Trading in Demand Response Management

Abstract: Increasing demand for electricity necessitates the use of efficient mechanisms for demand response management (DRM) in the existing smart grid (SG) system. In the Industry 4.0 era, the usage of information and communication technologies in the energy industry revolutionized the existing grid called SG, which provides a bi-directional flow of energy and data. To handle the energy demand of the consumers, DRM is crucial. It provides the active participation of consumers in the energy trading system (ETS) between consumers and service providers. The traditional energy trading system (TETS) relies on the centralized system or trusted third parties, which may act as a single point of failure. So, it is essential to equip the SG system with a secure energy trading system (SETS) to provide privacy and security to the consumer's data. In this direction, one of the emerging technology, called blockchain, can handle the issue as mentioned above, which is a chain of decentralized and distributed transaction ledger that is retained and maintained by each user. It performs peerto- peer (P2P) energy transactions among different consumers, such as individual houses, using smart contracts, and without a central control body. In a decentralized system, each consumer has its energy storage locally generated using renewable energy resources (RES). In this article, SETS, a blockchain-based decentralized ETS framework, is proposed for storing and processing the data generated from smart meters (SMs). In SETS, miner node is designated to validate the requests of energy requirements, dynamic pricing, and time of stay. Then, an energy transaction execution approach is designed for SETS. The evaluation results obtained show that SETS outperforms the TETS in terms of computation time and communication costs.