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Showing papers on "Dynamic pricing published in 2019"


Journal ArticleDOI
TL;DR: Basic and enhanced interaction strategies between a grid and buildings are developed using the Stackelberg game based on their identified Nash equilibria and the results show that the proposed basic interaction increased net profit by 8% and reduced demand fluctuation by about 40% and relieved the negative effects caused by prediction uncertainty.

119 citations


Journal ArticleDOI
TL;DR: This work proposes an alternative dynamic price experimentation policy that extends multiarmed bandit algorithms from statistical machine learning to include microeconomic choice theory.
Abstract: We propose an alternative dynamic price experimentation policy that extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory.

96 citations


Journal ArticleDOI
TL;DR: This paper reviews the most pertinent studies on optimization methods that address power scheduling problem in a smart home (PSPSH), and a critical analysis of state-of-the-art methods are provided.
Abstract: Optimizing the power demand for smart home appliances in a smart grid is the primary challenge faced by power supplier companies, particularly during peak periods, due to its considerable effect on the stability of a power system. Therefore, power supplier companies have introduced dynamic pricing schemes that provide different prices for a time horizon in which electricity prices are higher during peak periods due to the high power demand and lower during off-peak periods. The problem of scheduling smart home appliances at appropriate periods in a predefined time horizon in accordance with a dynamic pricing scheme is called power scheduling problem in a smart home (PSPSH). The primary objectives in addressing PSPSH are to reduce the electricity bill of users and maintain the stability of a power system by reducing the ratio of the highest power demand to the average power demand, known as the peak-to-average ratio, and to improve user comfort level by reducing the waiting time for appliances. In this paper, we review the most pertinent studies on optimization methods that address PSPSH. The reviewed studies are classified into exact algorithms and metaheuristic algorithms. The latter is categorized into single-based, population-based, and hybrid metaheuristic algorithms. Accordingly, a critical analysis of state-of-the-art methods are provided and possible future directions are also discussed.

89 citations


Journal ArticleDOI
TL;DR: The proposed EV power trading model uses the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser.
Abstract: In order to realize peer-to-peer (P2P) transactions between electric vehicles (EVs) in vehicle-to-grid (V2G) networks, we propose an EV power trading model based on blockchain and smart contract. Firstly, based on the blockchain and smart contract technology, a decentralized power trading model is proposed to realize the information equivalence and transparent openness of power trading. Then, considering the randomness and uncertainty of EV charging and discharging, the EV trading parties use the reverse auction mechanism based on dynamic pricing strategy to complete the transaction matching, which can not only improve the profit of the less competitive power seller, but also it can reduce the cost of the electricity purchaser. Finally, in order to verify the feasibility of our proposed scheme, V2G's EV power trading smart contract was designed, and the smart contract was released to Ethereum and simulated experiments were carried out. The effectiveness of the proposed scheme is verified by simulation experiments and comparison with traditional power trading schemes.

86 citations


Journal ArticleDOI
TL;DR: Omnichannel retail refers to a seamless integration of an e-commerce channel and a network of brick-and-mortar stores.
Abstract: Omnichannel retail refers to a seamless integration of an e-commerce channel and a network of brick-and-mortar stores. An example is cross-channel fulfillment, which allows a store to fulfill onlin...

82 citations


Journal ArticleDOI
TL;DR: In this article, the authors analyzed the effect of dynamic price variability on revenue maximization and found that higher price variability leads to higher hotel revenues, while the benefits from charging different prices for the same service (intertemporal price discrimination) and limiting the number of units available before the demand is known (inventory control) outweigh the potential negative effects of price unfairness and organizational culture.

82 citations


Journal ArticleDOI
TL;DR: In this article, the authors develop a dynamic game framework to explore the optimal pricing strategy when the firm sequentially introduces new generations of products to a market populated by strategic consumers with trade-in option offered.
Abstract: Many innovating firms use trade-in programs to encourage consumers’ repeat purchasing. They can choose between dynamic pricing and preannounced pricing strategies to mitigate the impacts of consumers’ strategic behavior. This paper develops a dynamic game framework to explore the optimal pricing strategy when the firm sequentially introduces new generations of products to a market populated by strategic consumers with trade-in option offered. Results show that under either pricing strategy, the firm has an incentive to sell the old generation products to new consumers in the second period if the salvage value of the old generation product is high enough. When consumers are sufficiently strategic, if both the innovation incremental value of the new generation product and the salvage value of the old generation product are low enough, the firm is better off following the preannounced pricing strategy. Besides, as the firm becomes more farsighted, the comparatively dominant position of preannounced pricing over dynamic pricing disappears gradually.

81 citations


Journal ArticleDOI
TL;DR: A two-period model in which a monopolistic original equipment manufacturer offers a trade-in programme to improve sales and collect used products, and the OEM can elect to remanufacture these used products and resell them to a secondary market is established.

79 citations


Journal Article
TL;DR: In this article, the authors study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time, and propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP), which leverages the sparsity structure of the high-dimensional model and obtains a logarithmic regret in $T.
Abstract: We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to minimize the regret, i.e., the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 \log d \cdot \log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (\log d + \log T))$.

76 citations


Journal ArticleDOI
TL;DR: In this paper, the authors examined the effects of pricing strategies, including price positioning and dynamic pricing, on an Airbnb listing's revenue with a particular interest on the performance difference between multi-unit and single-unit hosts.

75 citations


Journal ArticleDOI
TL;DR: This paper studies the potential benefits of responsive pricing and demand learning to sellers of seasonal fashion goods and finds that demand uncertainty is high at the beginning of the year in such markets.
Abstract: This paper studies the potential benefits of responsive pricing and demand learning to sellers of seasonal fashion goods. As typical in such markets, demand uncertainty is high at the beginning of ...

Journal ArticleDOI
Steffen Limmer1, Tobias Rodemann1
TL;DR: A framework for the setting of dynamic price offers for different charging deadlines and the scheduling of charging processes with the objectives of maximizing the charging station operator’s daily profit and reducing the peak of the electrical load is proposed and evaluated.

Journal ArticleDOI
TL;DR: This work studies a multiperiod dynamic pricing problem with contextual information, where the seller uses a misspecified demand model and the seller sequentially observes past demand, updates model parameters, and develops a new model to address this problem.
Abstract: We study a multiperiod dynamic pricing problem with contextual information, where the seller uses a misspecified demand model. The seller sequentially observes past demand, updates model parameters...

Proceedings Article
11 Apr 2019
TL;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.

Journal ArticleDOI
TL;DR: A multi-leader multi-follower Stackelberg game for energy trading is proposed by assuming EVs as the consumers and CSs as energy providers, and a dynamic pricing scheme designed by taking parameters such as electricity usage, time-of-use, location, and type of EVs.

Journal ArticleDOI
TL;DR: In this paper, the authors examined the impact of the strategic customer behavior in two-period pricing and the inventory decisions in a quick response system and found that strategic consumers may yield more revenues in specific scenarios.

Journal ArticleDOI
TL;DR: Simulation results have shown that proposed dynamic pricing scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases.

Journal ArticleDOI
TL;DR: A dynamic pricing strategy is proposed to minimize the average cost of the MEC system under the constraints on quality of service by adjusting the price constantly based on the current system state by solving the cost minimization problem efficiently.
Abstract: The idle computing resources of parked vehicles could be utilized to improve performance by assisting task executions in mobile edge computing (MEC) systems. As a result, the owner of a vehicle could be compensated, resulting in a win-win situation. A dynamic pricing strategy is proposed to minimize the average cost of the MEC system under the constraints on quality of service by adjusting the price constantly based on the current system state. To do so, a cost minimization problem is solved to obtain the optimal dynamic pricing strategy efficiently. Finally, the optimization results are validated with extensive simulations.

Journal ArticleDOI
TL;DR: Two numerical examples are conducted to demonstrate that the proposed dynamic pricing strategy with negative prices is effective in terms of attracting users as well as achieving a more balanced bike repositioning, especially when the number of bikes provided in the system is limited.
Abstract: To achieve bike relocation 1 through travellers’ spontaneous behaviour in dockless bike sharing systems, an innovative dynamic pricing scheme with negative prices is introduced. In normal situation, users pay a positive price to operators for using a bike. However, when imbalanced distribution of bikes occurs in the system, users who cycle from the oversupplied area to undersupplied area will receive monetary reward from the operator, i.e., negative pricing applies. A user equilibrium dynamic traffic assignment model is developed to capture travellers’ mode-path choice behaviour in response to the proposed dynamic pricing strategy. Travellers can either use a single transportation mode (e.g. walking, cycling and bus) or take multiple modes to complete their trips. The user equilibrium travel pattern is formulated as a variational inequality problem and then solved by a path-flow swapping algorithm. Two numerical examples are conducted to demonstrate that the proposed dynamic pricing strategy with negative prices is effective in terms of attracting users as well as achieving a more balanced bike repositioning, especially when the number of bikes provided in the system is limited.

Journal ArticleDOI
18 Sep 2019-Energies
TL;DR: This work aims at providing an overview and a categorization of the existing work in this growing field of research on time-varying pricing for electric vehicle charging, including user studies and the modeling of user preferences via utility functions.
Abstract: Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed.

Journal ArticleDOI
TL;DR: This study proposes a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction.

Journal ArticleDOI
15 Jan 2019-Energy
TL;DR: A two-layer optimization model that simultaneously determines dynamic pricing policy for the system operator and demand response strategies for the EV parking lots is developed, which minimizes the cost to consumers, while ensuring the system operators' revenue neutral status and addressing real-time price uncertainties.

Journal ArticleDOI
TL;DR: An optimization control strategy to achieve the minimum electricity cost based on the user response, equipment operating power, and dynamic pricing is proposed and the results show that the daily electricity cost is reduced and the peak-to-average ratio is reduced.
Abstract: Effective and adaptable household energy management system needs to be established to promote and implement demand response projects in smart grids The current household energy demand management strategy cannot provide users with a choice to ensure user comfort, its time sampling accuracy is not high enough, and the operation using the rated power results in a large deviation from the actual cost In order to solve these problems, this paper proposes an optimization control strategy to achieve the minimum electricity cost based on the user response, equipment operating power, and dynamic pricing The genetic algorithm is used for calculating the optimal operating parameters of each equipment by using the operating power The correctness and the high accuracy of the algorithm are verified by comparing with the loop search optimization algorithm The results show that the daily electricity cost is reduced by 290%, and the peak-to-average ratio is reduced by 362% after adopting the proposed strategy

Journal ArticleDOI
TL;DR: In this paper, the authors evaluate Finnish households' acceptance of hypothetical contracts and services aimed at increasing demand side flexibility and find that households' sensitivity to restrictions in electricity usage is much stronger than their sensitivity in heating.

Journal ArticleDOI
TL;DR: This paper presents a game-theoretic decentralized electric vehicle charging schedule for minimizing the customers’ payments, maximizing the grid efficiency, and providing the maximum potential capacity for ancillary services, and proposes a mechanism, which results in a full Nash Folk theorem.
Abstract: This paper presents a game-theoretic decentralized electric vehicle charging schedule for minimizing the customers’ payments, maximizing the grid efficiency, and providing the maximum potential capacity for ancillary services. Most of the available methods for electric vehicle charging assume that the customers are rational, there is a low-latency perfect two-way communication infrastructure without communication/computation limitation between the distribution company and all the customers, and they have perfect knowledge about the system parameters. To avoid these strong assumptions and preserve the customers’ privacy, we take advantage of the regret matching and the Nash Folk theorems. In the considered game, the players (customers) interact and communicate locally with only their neighbors. We propose a mechanism for this game, which results in a full Nash Folk theorem. We demonstrate and prove that the on-off charging strategy provides the maximum regulation capacity. However, our mechanism is quite general, takes into account the battery characteristics and degradation costs of the vehicles, provides a real-time dynamic pricing model, and supports the vehicle-to-grid and modulated charging protocols. Moreover, the developed mechanism is robust to the data disruptions and takes into account the long/short-term uncertainties.

Journal ArticleDOI
TL;DR: In this article, the authors introduce the fundamental revenue management (RM) models developed for air transportation, namely, capacity control and pricing models, and proceed to critically review the RM studies for container liner shipping services.
Abstract: The purpose of revenue management (RM) is to maximize revenue growth for a company by optimizing product/service availability and prices based on micro-level forecasting of customer behavior. Seat/cargo capacity control and air ticket/cargo pricing are two primary RM research topics that have yielded fruitful models and solution methods for air transportation, which have been used by airlines for around 50 years. However, the RM studies for container liner shipping services and their application are scant although the operations of airlines and container shipping lines are quite similar. We therefore introduce the fundamental RM models developed for air transportation, namely, capacity control and pricing models. Based on these models, we proceed to critically review the RM studies for container liner shipping services. Finally, we identify valuable future research directions in container shipping RM.

Journal ArticleDOI
TL;DR: In this article, a multi-period game-theoretic model was proposed to address dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles.
Abstract: Rapidly advancing on-demand ridesharing services, including those with self-driving technologies, hold the promise to revolutionize delivery of mobility. Yet, significant imbalance between spatiotemporal distributions of vehicle supply and travel demand poses a pressing challenge. This paper proposes a multi-period game-theoretic model that addresses dynamic pricing and idling vehicle dispatching problems in the on-demand ridesharing systems with fully compliant drivers/vehicles. A dynamic mathematical program with equilibrium constraints (MPEC) is formulated to capture the interdependent decision-making processes of the mobility service provider (e.g., regarding vehicle allocation) and travelers (e.g., regarding ride-sharing and travel path options). An algorithm based on approximate dynamic programming (ADP), with customized subroutines for solving the MPEC, is developed to solve the overall problem. It is shown with numerical experiments that the proposed dynamic pricing and vehicle dispatching strategy can help ridesharing service providers achieve better system performance (as compared with myopic policies) while facing spatial and temporal variations in ridesharing demand.

Journal ArticleDOI
TL;DR: This paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time.
Abstract: Small base stations (SBs) of fifth-generation (5G) cellular networks are envisioned to have storage devices to locally serve requests for reusable and popular contents by caching them at the edge of the network, close to the end users. The ultimate goal is to smartly utilize a limited storage capacity to serve locally contents that are frequently requested instead of fetching them from the cloud, contributing to a better overall network performance and service experience. To enable the SBs with efficient fetch-cache decision-making schemes operating in dynamic settings, this paper introduces simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions per time slot influence the content availability in future slots, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming. Under this generic formulation, first by considering stationary distributions for the costs as well as file popularities, an efficient reinforcement learning-based solver known as value iteration algorithm can be used to solve the emerging optimization problem. Later, it is shown that practical limitations on cache capacity can be handled using a particular instance of this generic dynamic pricing formulation. Under this setting, to provide a light-weight online solver for the corresponding optimization, the well-known reinforcement learning algorithm, $Q$ -learning, is employed to find optimal fetch-cache decisions. Numerical tests corroborating the merits of the proposed approach wrap up the paper.

Journal ArticleDOI
TL;DR: This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing, and implements dynamic pricing structures, which are applicable to various scenarios of energy trading.
Abstract: Autonomous electric vehicles (AEVs) are gaining ground around the world. They are equipped with intelligent decision-making capabilities that allow them to optimize on their battery usage and engage in energy trading for profit maximization. This paper envisions a market model, where energy aggregators considers buying and selling electricity from AEVs. In this system, the aggregators reside on cloudlets at the edge of a cloud computing system for prompt communication and negotiation with AEVs. Whether they are parked or en-route, AEVs can be crowdsourced to provide energy to consumers at needed times. This study proposes double auction models to incentivize AEV agents to participate in the market based on their time preferences and energy pricing. The proposed system implements dynamic pricing structures, which are applicable to various scenarios of energy trading. To evaluate the proposed system, this paper provides extensive theoretical analysis and simulation experiments to demonstrate that the proposed auctioning models are computationally efficient, truthful, individually rational, and budget balanced.

Journal ArticleDOI
23 May 2019
TL;DR: Simulation results indicate that the proposed scheme converges while enforcing the shared constraints and reduces the electricity cost to the customers with a quantifiable tradeoff between multiple objectives.
Abstract: Collaborative demand response management is an effective method to lower the peak-to-average ratio of demand and to facilitate the integration of locally distributed renewable energy resources to the electricity grid. The aggregator needs a holistic and privacy-preserving demand response management scheme to involve residential customers in a dynamic pricing market scenario. Using a quadratic function to model dynamic pricing, we propose a two-level distributed energy management scheme for a residential community to exploit the benefits of coordination among customers at the aggregator level and the smart devices at the customer level. In the proposed scheme, each customer wants to optimize the scheduling of its smart appliances, demand flexibility of air conditioning load, and energy storage strategies to minimize their expected cost, discomfort and appliance interruption. The aggregator, on the other hand, seeks to minimize the overall expected cost by optimizing customers energy demand and its energy storage strategies. The aggregator level optimization is formulated as a noncooperative Stackelberg equilibrium problem with shared constraints. Meanwhile, the customer level problem is formulated as a multiobjective optimization using different discomfort and interruption indicators to characterize various appliance preferences. We formulate iterative algorithms to obtain the appliance scheduling and storage strategies of the customers using genetic algorithm and to reach convergence. Simulation results indicate that the proposed scheme converges while enforcing the shared constraints and reduces the electricity cost to the customers with a quantifiable tradeoff between multiple objectives.