Fixed Pricing Scheme Research Articles

Capitalize Your Data: Optimal Selling Mechanisms for IoT Data Exchange

More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain IoT data quality and inflexible pricing interface. To resolve these issues, potential solutions could be to launch data demonstrations and release free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data actually used to enable flexible pricing. However, there is still no clear understanding of economic benefits of these mechanisms. In this paper, we design the optimal data selling mechanisms for IoT data exchange, and derive the following two results. First, whether to deploy a data demonstration and how much free sampling data to release depend on the extent of data consumers' inaccuracy perceptions for data quality, which varies over a wide range in IoT applications. We found that the data vendor has no incentive to conduct these strategies if data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for real-time and streaming IoT data exchange, it brings less economic benefits to the data vendor compared with the fixed pricing scheme, which sells the whole data set with a fixed price. We evaluate the optimal selling mechanisms on a real-world Taxi GPS data set, and evaluation results verify the insights derived from our theoretical analysis.

A Reinforcement Learning Approach to Price Cloud Resources With Provable Convergence Guarantees.

How to generate more revenues is crucial to cloud providers. Evidences from the Amazon cloud system indicate that "dynamic pricing" would be more profitable than "static pricing." The challenges are: How to set the price in real-time so to maximize revenues? How to estimate the price dependent demand so to optimize the pricing decision? We first design a discrete-time based dynamic pricing scheme and formulate a Markov decision process to characterize the evolving dynamics of the price-dependent demand. We formulate a revenue maximization framework to determine the optimal price and theoretically characterize the "structure" of the optimal revenue and optimal price. We apply the Q -learning to infer the optimal price from historical transaction data and derive sufficient conditions on the model to guarantee its convergence to the optimal price, but it converges slowly. To speed up the convergence, we incorporate the structure of the optimal revenue that we obtained earlier, leading to the VpQ-learning ( Q -learning with value projection) algorithm. We derive sufficient conditions, under which the VpQ-learning algorithm converges to the optimal policy. Experiments on a real-world dataset show that the VpQ-learning algorithm outperforms a variety of baselines, i.e., improves the revenue by as high as 50% over the Q -learning, speedy Q -learning, and adaptive real-time dynamic programming (ARTDP), and by as high as 20% over the fixed pricing scheme.

How to charge doctors and price medicines in a two-sided online healthcare platform with network externalities?

Platform business models are upending the value proposition of the companies in traditional industries, such as healthcare, through competitively pricing value-added services and products. In this study, we consider a unique, but important, on-demand two-sided service platform: a monopolistic online healthcare platform, which not only provides medical services to patients by connecting them with doctors, but also sells medical products to patients. A crucial operational decision for the platform is to set up a pricing scheme to encourage enough doctors and patients to participate on the platform. We consider two common pricing schemes in this study: fixed and linear pricing, in an empirically-grounded optimisation model that considers medicine pricing decisions together with network externalities. Our results show that the fixed pricing scheme almost always dominates the linear pricing scheme by providing the platform with higher expected profit. Our results also reveal the strong interdependence between the optimal doctor service pricing decision and optimal medicine pricing decision. However, these decisions are often made independently by different functional units of a firm, which could cause suboptimal platform financial performance. These results indicate the importance of pricing products and services in an integrated manner for maximising the profit of a product/service platform.

Exact solutions to a carsharing pricing and relocation problem under uncertainty

In this article we study the problem of jointly deciding carsharing prices and vehicle relocations. We consider carsharing services operating in the context of multi-modal urban transportation systems. Pricing decisions take into account the availability of alternative transport modes, and customer preferences with respect to these. In order to account for the inherent uncertainty in customer preferences, the problem is formulated as a mixed-integer two-stage stochastic program with integer decision variables at both stages. We propose an exact solution method for the problem based on the integer L-Shaped method which exploits an efficient exact algorithm for the solution of the subproblems. Tests on artificial instances based on the city of Milan illustrate that the method can solve, or find good solutions to, moderately sized instances for which a commercial solver fails. Furthermore, our results suggest that, by adjusting prices between different zones of the city, the operator can attract significantly more demand than with a fixed pricing scheme and that such a pricing scheme, coupled with a sufficiently large fleet, significantly reduces the relevance of staff-based relocations. A number of issues, that remain to be addressed in future research, are pointed out in our conclusions.

Dynamic system optimal traffic assignment with atomic users: Convergence and stability

In this study, we analyse the convergence and stability of dynamic system optimal (DSO) traffic assignment with fixed departure times. We first formulate the DSO traffic assignment problem as a strategic game wherein atomic users select routes that minimise their marginal social costs, called a ‘DSO game’. By utilising the fact that the DSO game is a potential game, we prove that a globally optimal state is a stochastically stable state under the logit response dynamics, and the better/best response dynamics converges to a locally optimal state. Furthermore, as an application of DSO assignment, we examine characteristics of the evolutionary implementation scheme of marginal cost pricing. Through theoretical comparison with a fixed pricing scheme, we found the following properties of the evolutionary implementation scheme: (i) the total travel time decreases smoother to an efficient traffic state as congestion externalities are perfectly internalised; (ii) a traffic state would reach a more efficient state as the globally optimal state is stabilised. Numerical experiments also suggest that these properties make the evolutionary scheme robust in the sense that they prevent a traffic state from going to worse traffic states with high total travel times.

Assessing Two-way and One-way Carsharing: an Agent-Based Simulation Approach

Carsharing companies can customize their service by adopting different pricing schemes and offers with the aim of increasing fleet use and profits. Different business models have been developed such as round-trip and one-way. It is clear that, even though many aspects of the business model and operations are the same, the different way in which these services are supplied leads to a diverse response from the users. In this work, we analyze how a fixed pricing scheme affects the behavior of the members of two different carsharing systems: two-way and one-way, explicitly considering their different income distributions to analyze social equity aspects. The dynamic pricing policy is simulated in MATSim, an agent-based simulator able to generate realistic mode choices based on individual activity-travel behavior. Scenarios with a synthetic population of carsharing members for the city of Berlin are analyzed. We aim to provide an experimental analysis that addresses the different behavior of different demand sectors, categorized by income, in function of the supply distributed on the territory. Simulation results show that the two services are not in competition between each other: the two-way service is used as a substitute for private cars while the one-way system is preferred from agents who choose to use multiple types of modes during the day. The response from the different income classes tends to be similar for both services since all the users within the same purchase power have the same degree of acceptance for both systems.

DwaRa: A Deep Learning-Based Dynamic Toll Pricing Scheme for Intelligent Transportation Systems

In Internet-of-Vehicles (IoV) ecosystems, intelligent toll gates (ITGs) connect nearby metropolitan cities through smart highways. At ITGs, existing solutions integrate blockchain (BC) and deep-learning schemes to leverage trusted and responsive analytics support for connected smart vehicles (CSVs) at ITGs. BC eliminates third-party intermediaries, and secures payments between vehicle owners (VO) and governing authorities (GA). Deep-Learning, on the other hand, facilitates accurate predictions for diverse and complex urban traffic conditions. However, due to fixed toll pricing schemes based on connected smart vehicles (CSV) type, VOs suffer from variable delays at different lanes due to dynamic congestion scenarios. To address the research gaps of such a fixed pricing schemes, we propose a BC-envisioned scheme DwaRa, that operates in three phases. In the first phase, future traffic is predicted based on Markov queues to balance the congestion at different lanes at ITGs efficiently. Then, we propose a novel spatially induced-long-short term memory (SI-LSTM) model to predict current traffic and weather based on historical repositories. Second, based on inputs by the Markov model, SI-LSTM, lane type, and vehicle type, a dynamic pricing algorithm is presented to improve the quality of experience (QoE) of the VO. Finally, based on dynamic price fixation between the VO and the GA, smart contracts (SCs) are executed and transactional data is secured through BC. The proposed scheme is compared against parameters like average mean-squared error (MSE), predicted traffic, scalability, interplanetary file system (IPFS) storage, computation (CC), and communication cost (CCM). At n = 100 test samples, and arrival rate β = 80, the obtained MSE is 0.0012, with a peak average value of 0.00526. The overall CC is 45.88 milliseconds (ms) and CCM is 53 bytes that indicate the proposed scheme efficacy against conventional approaches.

Predict and Match

We consider the problem of selling perishable items to a stream of buyers in order to maximize social welfare. A seller starts with a set of identical items, and each arriving buyer wants any one item, and has a valuation drawn i.i.d. from a known distribution. Each item, however, disappears after an a priori unknown amount of time that we term the horizon for that item. The seller knows the (possibly different) distribution of the horizon for each item, but not its realization till the item actually disappears. As with the classic prophet inequalities, the goal is to design an online pricing scheme that competes with the prophet that knows the horizon and extracts full social surplus (or welfare). Our main results are for the setting where items have independent horizon distributions satisfying the monotone-hazard-rate (MHR) condition. Here, for any number of items, we achieve a constant-competitive bound via a conceptually simple policy that balances the rate at which buyers are accepted with the rate at which items are removed from the system. We implement this policy via a novel technique of matching via probabilistically simulating departures of the items at future times. Moreover, for a single item and MHR horizon distribution with mean, we show a tight result: There is a fixed pricing scheme that has competitive ratio at most 2 - 1/μ, and this is the best achievable in this class. We further show that our results are best possible. First, we show that the competitive ratio is unbounded without the MHR assumption even for one item. Further, even when the horizon distributions are i.i.d. MHR and the number of items becomes large, the competitive ratio of any policy is lower bounded by a constant greater than 1, which is in sharp contrast to the setting with identical deterministic horizons.

Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa.

The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d’Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.

A Transactive Energy Framework for Coordinated Energy Management of Networked Microgrids With Distributionally Robust Optimization

Networked microgrids (MGs) are considered as an emerging grid design for the future distribution system (DS). The coordination of the networked MGs is critical in order to further enhance the operation efficiency and reliability of the system. In this paper, a transactive energy (TE) framework is proposed for the coordinated energy management of networked MGs in DS. Instead of direct coordination signals and fixed pricing schemes, the distribution network operator (DNO) organizes a transactive market with the MGs to coordinate the energy management in the operation. Further, a distributionally robust optimization (DRO)-based algorithm is developed to provide a robust solution of the detailed scheduling decisions in the proposed TE framework under uncertainty without being too conservative. Case studies with the proposed framework were conducted with the IEEE 33-bus system with three MGs and the IEEE 123-bus system with nine MGs. The results of the case studies show that the proposed TE-based framework can effectively coordinate the energy scheduling of the MGs. The operational cost of the DS is reduced significantly. Meanwhile, the proposed DRO-based algorithm provides a robust but not over-conservative solution for the operation decisions of the DNO and MGs in the proposed framework.

Optimization modeling for dynamic price based demand response in microgrids

Eco-friendly technologies regarding electricity production are urgent need of sustainable energy development. The renewable energy resources (RERs) have brought green revolution in mitigation of greenhouse gaseous emission resulted from traditional energy resources (TERs). Moreover, the effective utilization of these resources is influenced by pricing schemes which have limitations. Therefore, this paper aims at optimization modeling for dynamic price-based demand response (DR) which includes flexible and inflexible loads along with the effective utilization of RERs i.e. photovoltaics (PVs) and wind turbines (WTs) in a microgrid (MG). The optimization problem regarding profit maximization for loads (flexible and inflexible) is solved via particle swarm optimization (PSO). Two cases are used to evaluate the performance of proposed dynamic pricing scheme. The simulation results have shown that proposed scheme is suitable in term of profit and comfort for flexible and inflexible loads as compared to fixed pricing scheme in both cases. In addition, the dynamic pricing scheme is exemplified as plug and play devices because of its easy implementation in present market structure without any modification.

Dynamic congestion toll pricing strategies to evaluate the potential of route-demand diversion on toll facilities

Congestion pricing has been proved to be an effective traffic management measure to reduce traffic congestion in metropolitan regions. Its aim is to limit the access of traffic towards heavily congested areas, thus to force road users to shift to other means of transportation by imposing toll fares to the vehicles willing to access these areas. Previous urban congestion pricing schemes have been efficiently discouraging individual vehicles from accessing congested areas by reducing traffic and mitigating environmental emissions. This study presents a new methodology for a dynamic zone-based congestion pricing system in networks with heterogeneous users, where the variable fares are calculated based on traffic congestion and environmental emission indicators. Congestion pricing methodology aims to determine optimal toll fares based on traffic congestion, vehicle type, and emissions produced based on the London Emission Model (LEM) to ensure reduction of emissions in the network-wide level. Further, methodology is developed within Dynamic Traffic Assignment (DTA) framework with a microscopic simulation model, that enables behavioral adaptation of the drivers during the pricing period as well as realistic congestion propagation. The performance of the proposed methodology is evaluated against a potential fixed pricing scheme in a Leicester city network, with 9652 sections, 3757 nodes where pricing policy area covers four zones with radius of 1.5 km from the centre. Simulation outputs have shown decrease in traffic delay time and NOx emissions within the cordon area where the congestion pricing policy is applied. However, further strategic traffic management improvements are required to control the mode shift phenomena in the boundary area produced by the congestion policy.

VCG Auction-Based Dynamic Pricing for Multigranularity Service Composition

When a single service on its own cannot fulfill a sophisticated application, a composition of services is required. Existing methods mostly use a fixed-price scheme for service pricing and determine service allocation for composition based on a first-price auction. However, in a dynamic service market, it is difficult for service providers to determine a fixed price that is profitable while attractive to customers. Meanwhile, this mechanism cannot ensure that the providers who require the least cost to provide services would win the auction, because the pricing strategy of service providers is unpredictable. To address such issues, in this paper, we propose Vickrey-Clarke-Groves auction-based dynamic pricing for a generalized service composition. We consider fine-grained services as candidates for composition as well as coarse-grained ones. In our approach, service providers bid for services of different granularities in the composite service and based on received bids, a user decides a composition that minimizes the social cost while meeting quality constraints. Experimental results at last verify the feasibility and effectiveness of the proposed approach.

Charitable giving, suggestion, and learning from others: Pay-What-You-Want experiments at a coffee shop

We examine consumer behavior under Pay-What-You-Want (PWYW) pricing by conducting a series of field experiments that implemented different pricing schemes at a coffee shop: PWYW, PWYW with charitable giving, PWYW with charitable giving and a suggested price, and—for comparison—a regular fixed price group and a fixed price with giving group. We find that the PWYW scheme, when combined with charitable giving and a suggested price, yields net revenue as large as that under the fixed price scheme. We also find that consumers under PWYW with charitable giving are responsive to a suggested price and seek to learn from others.

Spot pricing in the Cloud ecosystem: A comparative investigation

Background: Spot pricing is considered as a significant supplement for building a full-fledged market economy for the Cloud ecosystem. However, it seems that both providers and consumers are still hesitating to enter the Cloud spot market. The relevant academic community also has conflicting opinions about Cloud spot pricing in terms of revenue generation.Aim: This work aims to systematically identify, assess, synthesize and report the published evidence in favor of or against spot-price scheme compared with fixed-price scheme of Cloud computing, so as to help relieve the aforementioned conflict.Method: We employed the systematic literature review (SLR) method to collect and investigate the empirical studies of Cloud spot pricing indexed by major electronic libraries.Results: This SLR identified 61 primary studies that either delivered discussions or conducted experiments to perform comparison between spot pricing and fixed pricing in the Cloud domain. The reported benefits and limitations were summarized to facilitate cost-benefit analysis of being a Cloud spot pricing player, while four types of theories were distinguished to help both researchers and practitioners better understand the Cloud spot market.Conclusions: This SLR shows that the academic community strongly advocates the emerging Cloud spot market. Although there is still a lack of practical and easily deployable market-driven mechanisms, the overall findings of our work indicate that spot pricing plays a promising role in the sustainability of Cloud resource exploitation.

Recent research advances in cloud computing and big data

Recent research advances in cloud computing and big data

New bounds for the balloon popping problem

We reconsider the balloon popping problem, an intriguing combinatorial problem introduced in order to bound the competitiveness of ascending auctions with anonymous bidders with respect to the best fixed-price scheme. Previous works show that the optimal solution for this problem is in the range (1.6595,2). We give a new lower bound of $$1.68$$ 1.68 and design an $$O(n^5)$$ O ( n 5 ) algorithm for computing upper bounds as a function of the number of bidders $$n$$ n . Our algorithm provides an experimental evidence that the correct upper bound is a constant smaller than $$2$$ 2 , thus disproving a currently believed conjecture, and can be used to test the validity of a new conjecture we propose, according to which the upper bound would decrease to $$\pi ^2/6+1/4\approx 1.8949$$ ? 2 / 6 + 1 / 4 ? 1.8949 .

Fair Benchmarking for Cloud Computing systems

The performance of Cloud systems is a key concern, but has typically been assessed by the comparison of relatively few Cloud systems, and often on the basis of just one or two features of performance. In this paper, we discuss the evaluation of four different Infrastructure as a Service (IaaS) Cloud systems --- from Amazon, Rackspace, and IBM --- alongside a private Cloud installation of OpenStack, using a set of five so-called micro-benchmarks to address key aspects of such systems. The results from our evaluation are offered on a web portal with dynamic data visualization. We find that there is not only variability in performance by provider, but also variability, which can be substantial, in the performance of virtual resources that are apparently of the same specification. On this basis, we can suggest that performance-based pricing schemes would seem to be more appropriate than fixed-price schemes, and this would offer much greater potential for the Cloud Economy.

Evaluating Conservation Auctions with Unknown Bidder Costs: The Scottish Fishing Vessel Decommissioning Program

As an alternative to the more traditional fixed-price schemes, governments can run auctions to purchase environmental services from private agents. Governments have so far chosen the discriminatory price (DP) over the uniform price (UP) format. Theoretical and experimental studies have concluded that the DP usually performs better than the UP in terms of cost-effectiveness. Using field data from two fishing vessel decommissioning auctions in Scotland, we find ambiguous results regarding relative DP and UP performances. A novel approach for estimating the underlying bidder costs shows that bid shading and cost heterogeneity can each determine relative auction performance. (JEL Q22, Q28)

A negotiation-based scheme for service level pricing for wireless access

The Next Generation Network (NGN) will enable service providers to offer broadband services with differentiated quality to their subscribers. In this way, the providers will be able to satisfy and retain the critical segment of the top-demanding customers, who generate the largest share of the provider’s revenues. However, service pricing is as important as service quality. In order to offer competitive prices, bilateral per service negotiation between the customer and the supplier is an interesting practical solution that can provide better results than rigid offers based on fixed price-rate lists. In the application scenario of Broadband Wireless Access, the supplier can adapt its offer of wireless access bandwidth to the dynamics of customer-generated traffic, giving more bandwidth per session when traffic is low and less bandwidth per session when traffic is high. We present and study the performance of an algorithm for automatic service level pricing in which two agents negotiate over the service transmission rate and the price on behalf of the user and of the provider. The performance of the proposed negotiation scheme is evaluated with simulations. The paper also compares our algorithm to the fixed-price scheme and to an auction-based strategy based on the Vickrey scheme.