Showing papers on "Dynamic pricing published in 2016"

The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Abstract: Recent platforms, like Uber and Lyft, offer service to consumers via “self-scheduling” providers who decide for themselves how often to work. These platforms may charge consumers prices and pay providers wages that both adjust based on prevailing demand conditions. For example, Uber uses a “surge pricing” policy, which pays providers a fixed commission of its dynamic price. We find that the optimal contract substantially increases the platform's profit relative to contracts that have a fixed price or fixed wage (or both) and although surge pricing is not optimal, it generally achieves nearly the optimal profit. Despite its merits for the platform, surge pricing has been criticized in the press and has garnered the attention of regulators due to concerns for the welfare of providers and consumers. However, we find that providers and consumers are generally better off with surge pricing because providers are better utilized and consumers benefit both from lower prices during normal demand and expanded access to service during peak demand. We conclude, in contrast to popular criticism, that all stakeholders can benefit from the use of surge pricing on a platform with self-scheduling capacity.

Load forecasting, dynamic pricing and DSM in smart grid: A review

Abstract: Load forecasting (LF) plays important role in planning and operation of power systems. It is envisioned that future smart grids will utilize LF and dynamic pricing based techniques for effective Demand Side Management (DSM). This paper presents a comprehensive and comparative review of the LF and dynamic pricing schemes in smart grid environment. Real Time Pricing (RTP), Time of Use (ToU) and Critical Peak Pricing (CPP) are discussed in detail. Two major categories of LF: mathematical and artificial intelligence based computational models are elaborated with subcategories. Mathematical models including auto recursive, moving average, auto recursive moving average, auto recursive integrated moving average, exponential smoothing, iterative reweighted mean square, multiple regression, etc. used for effective DSM are discussed. Neural networks, fuzzy logic, expert systems of the second major category of LF models have also been described.

Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform

Abstract: In many markets, new technologies allow traditional jobs to be divided into discrete tasks that are widely distributed across workers and dynamically priced given prevailing supply and demand conditions. This "sharing" or "gig" economy represents a more flexible work system, and is most common in two-sided markets in which a firm acts as a platform to connect service providers and consumers. One prominent example of this is the ride-sharing company Uber, which connects riders and driver-partners, and dynamically prices trips using a system known as "surge" pricing. In this talk, I discuss the practical problems of designing such a dynamic pricing system, how that dynamic pricing coordinates workers who can now earn compensation on a flexible schedule, and more broadly how the "gig" economy is evolving and growing as a form of market organization.

Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

Abstract: In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.

Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes:

Abstract: The market potential of a fleet of shared autonomous electric vehicles (SAEVs) is explored by using a multinomial logit mode choice model in an agent-based framework and different fare settings. The mode share of SAEVs in the simulated midsize city (modeled roughly after Austin, Texas) is predicted to lie between 14% and 39% when the SAEVs compete with privately owned, manually driven vehicles and city bus service. The underlying assumptions are that SAEVs are priced between $0.75/mi and $1.00/mi, which delivers significant net revenues to the fleet owner–operator under all modeled scenarios; that they have an 80-mi range and that Level 2 charging infrastructure is available; and that automation costs are up to $25,000 per vehicle. Various dynamic pricing schemes for SAEV fares indicate that specific fleet metrics can be improved with targeted strategies. For example, pricing strategies that attempt to balance available SAEV supply with anticipated trip demand can decrease average wait times by 19% to 23%...

Co-Optimization of Power and Reserves in Dynamic T&D Power Markets With Nondispatchable Renewable Generation and Distributed Energy Resources

Abstract: Marginal-cost-based dynamic pricing of electricity services, including real power, reactive power, and reserves, may provide unprecedented efficiencies and system synergies that are pivotal to the sustainability of massive renewable generation integration. Extension of wholesale high-voltage power markets to allow distribution network connected prosumers to participate, albeit desirable, has stalled on high transaction costs and the lack of a tractable market clearing framework. This paper presents a distributed, massively parallel architecture that enables tractable transmission and distribution locational marginal price (TD electric vehicle (EV) battery charging and storage; heating, ventilating, and air conditioning (HVAC) and combined heat & power (CHP) microgenerators; computing; volt/var control devices; grid-friendly appliances; smart transformers; and more. The proposed iterative distributed architecture can discover T&DLMPs while capturing the full complexity of each participating DER’s intertemporal preferences and physical system dynamics.

Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods

Abstract: In this paper, we study the impact of consumer-generated quality information (e.g., consumer reviews) on a firm’s dynamic pricing strategy in the presence of strategic consumers. Such information is useful, not only to the consumers that have not yet purchased the product but also to the firm. The informativeness of the consumer-generated quality information depends, however, on the volume of consumers who share their opinions and, thus, depends on the initial sales volume. Hence, via its initial price, the firm not only influences its revenue but also controls the quality information flow over time. The firm may either enhance or dampen the quality information flow via increasing or decreasing initial sales. The corresponding pricing strategy to steer the quality information flow is not always intuitive. Compared to the case without consumer-generated quality information, the firm may reduce the initial sales and lower the initial price. Interestingly, the firm may get strictly worse off due to the consu...

An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace

Abstract: The rise of e-commerce has unlocked practical applications for algorithmic pricing (also called dynamic pricing algorithms), where sellers set prices using computer algorithms. Travel websites and large, well known e-retailers have already adopted algorithmic pricing strategies, but the tools and techniques are now available to small-scale sellers as well. While algorithmic pricing can make merchants more competitive, it also creates new challenges. Examples have emerged of cases where competing pieces of algorithmic pricing software interacted in unexpected ways and produced unpredictable prices, as well as cases where algorithms were intentionally designed to implement price fixing. Unfortunately, the public currently lack comprehensive knowledge about the prevalence and behavior of algorithmic pricing algorithms in-the-wild. In this study, we develop a methodology for detecting algorithmic pricing, and use it empirically to analyze their prevalence and behavior on Amazon Marketplace. We gather four months of data covering all merchants selling any of 1,641 best-seller products. Using this dataset, we are able to uncover the algorithmic pricing strategies adopted by over 500 sellers. We explore the characteristics of these sellers and characterize the impact of these strategies on the dynamics of the marketplace.

Dynamic Pricing and Distributed Energy Management for Demand Response

Abstract: The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the tradeoffs between consumer surplus and retail profit is obtained. The Pareto front of achievable tradeoffs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.

Behavior-Based Pricing: An Analysis of the Impact of Peer-Induced Fairness

Abstract: Firms tracking consumer purchase information often use behavior-based pricing (BBP), i.e., price discriminate between consumers based on preferences revealed from purchase histories. However, behavioral research has shown that such pricing practices can lead to perceptions of unfairness when consumers are charged a higher price than other consumers for the same product. This paper studies the impact of consumers’ fairness concerns on firms’ behavior-based pricing strategy, profits, consumer surplus, and social welfare. Prior research shows that BBP often yields lower profits than profits without customer recognition or behavior-based price discrimination. By contrast, we find that firms’ profits from conducting BBP increase with consumers’ fairness concerns. When fairness concerns are sufficiently strong, practicing BBP is more profitable than without customer recognition. However, consumers’ fairness concerns decrease consumer surplus. In addition, when consumers’ fairness concerns are sufficiently stron...

An Online Mechanism for Resource Allocation and Pricing in Clouds

Abstract: Cloud providers provision their various resources such as CPUs, memory, and storage in the form of virtual machine (VM) instances which are then allocated to the users. The users are charged based on a pay-as-you-go model, and their payments should be determined by considering both their incentives and the incentives of the cloud providers. Auction markets can capture such incentives, where users name their own prices for their requested VMs. We design an auction-based online mechanism for VM provisioning, allocation, and pricing in clouds that considers several types of resources. Our proposed online mechanism makes no assumptions about future demand of VMs, which is the case in real cloud settings. The proposed online mechanism is invoked as soon as a user places a request or some of the allocated resources are released and become available. The mechanism allocates VM instances to selected users for the period they are requested for, and ensures that the users will continue using their VM instances for the entire requested period. In addition, the mechanism determines the payment the users have to pay for using the allocated resources. We prove that the mechanism is incentive-compatible, that is, it gives incentives to the users to reveal their actual requests. We investigate the performance of our proposed mechanism through extensive experiments.

Choice-Based Demand Management and Vehicle Routing in E-Fulfillment

Abstract: Attended home delivery services face the challenge of providing narrow delivery time slots to ensure customer satisfaction, while keeping the significant delivery costs under control. To that end, a firm can try to influence customers when they are booking their delivery time slot so as to steer them toward choosing slots that are expected to result in cost-effective schedules. We estimate a multinomial logit customer choice model from historic booking data and demonstrate that this can be calibrated well on a genuine e-grocer data set. We propose dynamic pricing policies based on this choice model to determine which and how much incentive (discount or charge) to offer for each time slot at the time a customer intends to make a booking. A crucial role in these dynamic pricing problems is played by the delivery cost, which is also estimated dynamically. We show in a simulation study based on real data that anticipating the likely future delivery cost of an additional order in a given location can lead to significantly increased profit as compared with current industry practice.

Optimal dynamic pricing and preservation technology investment for deteriorating products with reference price effects

Abstract: Marketing and consumer behavior literature has empirically demonstrated that reference prices play a critical role in customer purchase decisions In this paper, we propose a joint dynamic pricing and preservation technology investment model for a deteriorating inventory system with time-and-price sensitive demand and reference price effects A generalized model is presented to jointly determine the optimal selling price, preservation technology investment and replenishment strategies that maximize the retailer's total profit over a finite planning horizon Beginning with mild assumptions, we derive theoretical results to demonstrate the existence of an optimal solution for the deteriorating inventory problem, and reveal the sensitivities of optimal pricing and preservation technology investment decisions to an initial reference price A simple iterative algorithm is then used to solve the proposed model by employing the theoretical results Numerical examples and sensitivity analysis are then provided to illustrate the features of the proposed model Finally, concluding remarks are offered

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Abstract: The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user's visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection.

Modeling and optimization of multimodal urban networks with limited parking and dynamic pricing

Abstract: Cruising-for-parking constraints mobility in urban networks. Car-users may have to cruise for on-street parking before reaching their destinations. The accessibility and the cost of parking significantly influence people's travel behavior (such as mode choice, or parking facility choice between on-street and garage). The cruising flow causes delays eventually to everyone, even users with destinations outside limited parking areas. It is therefore important to understand the impact of parking limitation on mobility, and to identify efficient parking policies for travel cost reduction. Most existing studies on parking fall short in reproducing the dynamic spatiotemporal features of traffic congestion in general, lack the treatment of dynamics of the cruising-for-parking phenomenon, or require detailed input data that are typically costly and difficult to collect. In this paper, we propose an aggregated and dynamic approach for modeling multimodal traffic with the treatment on parking, and utilize the approach to design dynamic parking pricing strategies. The proposed approach is based on the Macroscopic Fundamental Diagram (MFD), which can capture congestion dynamics at network-level for single-mode and bi-modal (car and bus) systems. A parsimonious parking model is integrated into the MFD-based multimodal modeling framework, where the dynamics of vehicular and passenger flows are considered with a change in the aggregated behavior (e.g. mode choice and parking facility choice) caused by cruising and congestion. Pricing strategies are developed with the objective of reducing congestion, as well as lowering the total travel cost of all users. A case study is carried out for a bi-modal city network with a congested downtown region. An elegant feedback dynamic parking pricing strategy can effectively reduce travel delay of cruising and the generic congestion. Remarkably, such strategy, which is applicable in real-time management with limited available data, is fairly as efficient as a dynamic pricing scheme obtained from system optimum conditions and a global optimization with full information about the future states of the system. Stackelberg equilibrium is also investigated in a competitive behavior between different parking facility operators. Policy indications on on-street storage capacity management and pricing are provided.

Dynamic Pricing Design for Demand Response Integration in Power Distribution Networks

Abstract: This paper presents optimal pricing design for demand response (DR) integration in the distribution network. In particular, we study the energy scheduling problem for a load serving entity (LSE) that serves two types of loads, namely inflexible and flexible loads. Inflexible loads are charged under a regular pricing tariff while flexible loads enjoy a dynamic pricing tariff that ensures cost saving for them. Moreover, flexible loads are assumed to be aggregated by several DR aggregators. The interaction between the LSE and its customers is formulated as a bilevel optimization problem where the LSE is the leader and DR aggregators are the followers. The optimal solution of this problem corresponds to the optimal pricing tariff for flexible loads. The key advantage of the proposed model is that it can be readily implemented thanks to its compatibility with existing pricing structures in the retail market. Extensive numerical results show that the proposed approach provides a win-win solution for both the LSE and its customers.

Residential Load Modeling of Price-Based Demand Response for Network Impact Studies

Abstract: This paper presents a comprehensive low-voltage residential load model of price-based demand response (DR). High-resolution load models are developed by combing Monte Carlo Markov chain bottom–up demand models, hot water demand models, discrete state space representation of thermal appliances, and composite time-variant electrical load models. Price-based DR is then modeled through control algorithms for thermostatically controlled loads, optimal scheduling of wet appliances, and price elasticity matrices for representing the inherent elastic response of the consumer. The developed model is used in a case study to examine the potential distribution network impacts of the introduction of dynamic pricing schemes. The effects of cold load pick-up, rebound peaks, decrease in electrical and demand diversity, and impacts on loading and voltage are presented.

Dynamic Pricing and Traffic Engineering for Timely Inter-Datacenter Transfers

Abstract: Neither traffic engineering nor fixed prices (e.g., \$/GB) alone fully address the challenges of highly utilized inter-datacenter WANs. The former offers more service to users who overstate their demands and poor service overall. The latter offers no service guarantees to customers, and providers have no lever to steer customer demand to lightly loaded paths/times. To address these issues, we design and evaluate Pretium -- a framework that combines dynamic pricing with traffic engineering for inter-datacenter bandwidth. In Pretium, users specify their required rates or transfer sizes with deadlines, and a price module generates a price quote for different guarantees (promises) on these requests. The price quote is generated using internal prices (which can vary over time and links) which are maintained and periodically updated by Pretium based on history. A supplementary schedule adjustment module gears the agreed-upon network transfers towards an efficient operating point by optimizing time-varying operation costs. Experiments using traces from a large production WAN show that Pretium improves total system efficiency (value of routed transfers minus operation costs) by more than 3.5X relative to current usage-based pricing schemes, while increasing the provider profits by 2X.

Dynamic Stochastic Inventory Management with Reference Price Effects

Abstract: We analyze the joint inventory and pricing decisions of a firm when demand depends on not only the current selling price but also a memory-based reference price and customers are loss averse. The p...

Dynamic Pricing with Demand Covariates

Abstract: We consider a firm that sells a product over T periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. In practice, this problem is commonly solved via greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue is used for the next period. The performance is measured by the regret, which is the expected revenue compared to an oracle that knows the true demand function. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal and introduced optimal algorithms which integrate forced price-dispersion with GILS. Here, we consider this dynamic pricing problem in a data-rich environment. We assume that the firm has access to demand covariates which may be predictive of the demand and prove that GILS achieves an asymptotically optimal regret of order log(T). We also show that the asymptotic optimality of GILS holds even when the covariates are uninformative. We validate our results via simulations on synthetic and real data.

Online Network Revenue Management Using Thompson Sampling

Abstract: We consider a network revenue management problem where an online retailer aims to maximize revenue from multiple products with limited inventory constraints. As common in practice, the retailer does not know the consumer's purchase probability at each price and must learn the mean demand from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints into the model and algorithm. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting. More broadly, our paper contributes to the literature on the multi-armed bandit problem with resource constraints, since our algorithm applies directly to this setting when the inventory constraint is interpreted as general resource constraints.

An overview of pricing models for revenue management

Abstract: In this paper we examine the research and results of dynamic pricing policies and their relation to Revenue Management. The survey is based on a generic Revenue Management problem in which a perishable and non-renewable set of resources satisfy stochastic price-sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: how to dynamically set the menu and the quantity of products and their corresponding prices in order to maximize the total revenue over the selling horizon.

Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems

Abstract: Crowdsourcing markets have emerged as a popular platform for matching available workers with tasks to complete. The payment for a particular task is typically set by the task's requester, and may be adjusted based on the quality of the completed work, for example, through the use of "bonus" payments. In this paper, we study the requester's problem of dynamically adjusting quality-contingent payments for tasks. We consider a multi-round version of the well-known principal-agent model, whereby in each round a worker makes a strategic choice of the effort level which is not directly observable by the requester. In particular, our formulation significantly generalizes the budget-free online task pricing problems studied in prior work. We treat this problem as a multi-armed bandit problem, with each "arm" representing a potential contract. To cope with the large (and in fact, infinite) number of arms, we propose a new algorithm, AgnosticZooming, which discretizes the contract space into a finite number of regions, effectively treating each region as a single arm. This discretization is adaptively refined, so that more promising regions of the contract space are eventually discretized more finely. We analyze this algorithm, showing that it achieves regret sublinear in the time horizon and substantially improves over non-adaptive discretization (which is the only competing approach in the literature). Our results advance the state of art on several different topics: the theory of crowdsourcing markets, principal-agent problems, multi-armed bandits, and dynamic pricing.