Adaptive Pricing Research Articles

Online Learning and Pricing for Service Systems with Reusable Resources

We consider a price-based revenue management problem with finite reusable resources over a finite time horizon. Stochastically arrived customers request an exponentially distributed service time and may balk and renege given insufficient resource. The resource unit, upon completion of serving one customer, can be released to serve the next customer immediately. The arrival, service, balking, and reneging rates all depend on the price being offered. In this paper, we assume that the firm does not know the mappings between these rates and prices, and thus it makes adaptive pricing decisions in each period based only on past sales to maximize the cumulative revenue. We propose two new multi-armed bandit (MAB) based learning algorithms, termed Batch Upper Confidence Bound (BUCB) algorithm and Batch Thompson Sampling (BTS) algorithm, for finding near-optimal pricing policies. Compared with prior pricing and MAB literature, the salient difficulties of this problem lie in (i) the unknown rate-and-price mapping information, (ii) the dynamic nature of reusable resources being committed over time, (iii) the transient behavior of the service system when the price changes, and (iv) unbounded and heavy-tailed distributions of observed random variables. Our proposed algorithms contain a Warm-up Phase to eliminate the heavy-tail effects and a Learning Phase to identify the optimal price. Our algorithms separate the Learning Phase into successive operational batches and select a price from a prescribed set in each batch using past sales collected in previous batches. The performance measure is cumulative regret, which is the difference between the revenue attained by our approach and by a clairvoyant optimal pricing policy under full distributional information. We prove that the cumulative regret is $O(\sqrt{PT\log (T)})$, where $T$ is the total number of time periods and $P$ is the cardinality of the feasible price set, and the result matches the lower bound up to a logarithmic factor. As an intermediate step, we also develop a coupling analysis for analyzing the time for a queue to reach the steady state from an empty state or from a steady state under another set of system parameters. Our numerical experiments demonstrate and confirm the efficacy of the proposed BUCB and BTS algorithms.

A Research on QoS Optimization in 4G Cellular Networks

cellular system conveys sight and sound website online traffic productively during ongoing transmission. in view that this device transmits blended media information, there may be a prerequisite to adjust the management price as indicated via the gadget situations an excellent manner to hold up fruitful device hobby. in the end, there is a need to allot the assets capably. Nature of provider (QoS) improvement within the sort of system is additionally trying out specially in 4G cellular systems. severa plans were proposed for QoS development in cellular structures which be a part of name Admission control (CAC) and electricity control Scheme. Be that as it is able to, CAC with electricity control isn't always sufficient to offer the most ideal asset use. alongside the ones lines, it is straightforward to build up an powerful QoS primarily based definitely resource Allocation and Scheduling Scheme for normal sight and sound site visitors. The primary objective of proposed studies paintings is to build up a nice of provider based resource Allocation and Adaptive rate Scheduling (QRAAS) plot in cellular systems to improve prepare dependability, boom the framework limit and enhance framework proficiency and therefore to improve best of service in cell systems. To execute the formerly stated QRAAS conspire, resource Allocation based definitely call Admission manage (RACAC) plan and Adaptive price Scheduling (ARS) plot were created.

A User-Oriented Pricing Design for Demand Response in Smart Grid

Demand response (DR) programs are designed to affect the energy consumption behavior of end-users in smart grid. However, most existing pricing designs for DR programs ignore the influence of end-users’s diversity and personal preference. Thus, in this paper, we investigate an incentive pricing design based on the utility maximization rule with consideration of end-users’ preference and appliances’ operational patterns. In particular, the utility company determines the pricing policy by trading off the budget revenue and social obligation, while each end-user aims to maximize their own utility profits with high satisfaction level by scheduling multiclass appliances. We formulate the conflict and cooperative relationship between the utility company and end-users as a Stackelberg game, and the equilibrium points are obtained by the backward induction method, which exists and is unique. At the equilibrium, the utility company adopts real-time pricing (RTP) scheme to coordinate end-users to fulfill the benefit of themselves, i.e., under such price, end-users automatically maximize overall utility profits of the overall system. We propose a distributed algorithm and an adaptive pricing scheme for the utility company and end-users to jointly achieve the best performance of the entire system. Finally, extensive simulation results based on real operation data show the effectiveness of the proposed scheme.

Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks

Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers’ incomes, improving riders’ experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing “signals.” We have conducted an extensive experimental evaluation upon over 4.0× 10 8 MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20% ride prediction accuracy and 30% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks.

Mitigating Interference via Power Control for Two-Tier Femtocell Networks: A Hierarchical Game Approach

In this paper, we focus on the interference mitigation problem in two-tier femtocell networks where the femtocell users (FUEs), share the same frequency band with the existing macrocell users (MUEs). More specifically, we formulate the problem as a multiple-leader-multiple-follower non-cooperative hierarchical game to maximize the utilities of both MUEs and FUEs by obtaining the optimal powers. We theoretically prove that there exists a unique Nash equilibrium in the hierarchical game. We then develop a distributed iterative power update strategy to allocate transmission powers efficiently. In addition, we take advantage of dynamic and adaptive pricing and maximum power adaptation to manage the interferences introduced by the transmission powers of FUEs and mitigate the near-far effect in the networks. Furthermore, we propose an admission control algorithm to alleviate the network congestion when the quality of service of FUEs cannot be guaranteed. We carry out extensive simulations to demonstrate that our proposed scheme is able to maximize the utilities of both MUEs and FUEs, while guaranteeing MUEs' required signal-to-interference-plus-noise ratios and admitting as many FUEs as possible to share network resources.

AG‐DPA: Assignment game–based dynamic power allocation in multibeam satellite systems

SummaryDynamic power allocation is the key technology to maintain the link quality and improve the system throughput in multibeam satellite systems. Many numerical optimization algorithms have been proposed to optimize the power allocation schemes among beams. However, current metaheuristic algorithms, most of which are off‐line iterative methods, are not appropriate in nonuniform traffic demands and time‐varying channels due to the high computational complexity. To solve this problem, an assignment game–based dynamic power allocation (AG‐DPA) is proposed to achieve the suboptimality with low complexity in multibeam satellite systems. The key idea of the proposed AG‐DPA is to model the DPA problem into an assignment game model where the competitive equilibrium is achieved. Further, an adaptive price factor is introduced to make a trade‐off between algorithm performance and complexity. Simulation results show the effectiveness of the proposed AG‐DPA algorithm.

Learning and managing stochastic network traffic dynamics with an aggregate traffic representation

This study estimates and manages the stochastic traffic dynamics in a bi-modal transportation system, and gives hints on how increasing data availability in transport and cities can be utilized to estimate transport supply functions and manage transport demand simultaneously. In the bi-modal system, travelers’ mode choices are based on their perceptions of the two travel modes: driving or public transit. Some travelers who have access to real-time road (car) traffic information may shift their mode based on the information received (note that real-time information about public transit departures/arrivals is not considered here). For the roadway network, the within-day traffic evolution is modeled through a Macroscopic Fundamental Diagram (MFD), where the flow dynamics exhibits a certain level of uncertainty. A non-parametric approach is proposed to estimate the MFD. To improve traffic efficiency, we develop an adaptive pricing mechanism coupled with the learned MFD. The adaptive pricing extends the study of Liu and Geroliminis (2017) to the time-dependent case, which can better accommodate temporal demand variations and achieve higher efficiency. Numerical studies are conducted on a one-region theoretical city network to illustrate the dynamic evolution of traffic, the MFD learning framework, and the efficiency of the adaptive pricing mechanism.

Research and systematization of specific expressions of the adaptation of retail trade enterprise

The object of research is the process of systematization of specific manifestations of the retailer adaptation. The responsiveness of the retailer to various changes in the external environment requires flexibility and adaptability in making various management decisions and is determined by its adaptation potential. One of the most problematic places is the identification of specific types of adaptation for enterprises in the trade sector. Systematization of the specific manifestations of the adaptation of an enterprise is complicated by the lack of a single view in the economic literature, which hampers the process of forming an effective adaptation policy in commercial enterprises.The analysis of existing approaches to the classification of types of adaptation. The main accents of adaptation of business structures are defined. The main specific manifestations of the adaptation of the enterprise are systematized and characterized. Expanded criteria for the enterprise adaptation by introducing additional ones: according to the level and degree of planning, if necessary, investment support and according to the degree of recoupment of adaptation costs. The study identified the characteristic features of the adaptation potential of enterprises in the retail industry from enterprises in other sectors of the economy: sensitivity to the variability of input resources and the requirements and demands of consumers.The structure of the marketing adaptation of the retail enterprise has been determined and the commodity, price, spatial and communication types of the marketing adaptation of the retail enterprise have been highlighted, taking into account their industry specifics. These types of adaptation allow the retailer to more reasonably identify a set of adaptation solutions, the implementation of which ensures the adequacy and retailer adaptability to the conditions of market demand, customer requirements and demands. To determine the basic directions of marketing adaptation of a retail enterprise, key characteristics of the elements of the 4P marketing mix have been identified. Compared with the existing approaches to the classification, the proposed structuring of the marketing adaptation of a retailer creates the opportunity to meet the demands and demands of consumers and form a positive consumer mood. The implementation of marketing adaptation, respectively, elements of the 4 P marketing mix, will contribute to the growth of the main resulting indicators of the retail enterprise.

Backup or Reliability Improvement Strategy for a Manufacturer Facing Heterogeneous Consumers in a Dynamic Supply Chain

This paper examines a manufacturer's supply management strategies for mitigating yield risk in a complex dynamic supply chain. Two strategies can be adopted for the manufacturer: backup and reliability improvement. Consumers may select to leave (instant consumers) or wait (delaying consumers) when they confront the manufacturer's insufficient inventory. Utilizing the method of multi-agent modeling, a manufacturer and a supplier are modeled as the intelligent agents with the reinforcement learning behavior. The study shows that: 1) when the number of instant consumers is small, reliability improvement strategy should be selected; otherwise, the manufacturer should adopt a backup strategy; 2) only when mean yield is large enough, reliability improvement strategy is the optimal choice; and 3) if yield uncertainty is small, the manufacturer should choose reliability improvement strategy; otherwise, it is suitable to use a backup strategy. In addition, when the main supplier can determine its own wholesale price, it is found that: 1) when the mean yield is small, a lower wholesale price should be designed for the main supplier, to induce higher order quantity under backup strategy; and 2) the impact of yield uncertainty on the manufacturer's supply management strategy can be changed by the main supplier's adaptive pricing behavior.

Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results

Radio access network (RAN) slicing is an effective methodology to dynamically allocate networking resources in 5G networks. One of the main challenges of RAN slicing is that it is provably an NP-Hard problem. For this reason, we design near-optimal low-complexity distributed RAN slicing algorithms. First, we model the slicing problem as a congestion game, and demonstrate that such game admits a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of the NE, i.e., the efficiency of the NE as compared with the social optimum, and demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two fully-distributed algorithms that provably converge to the unique NE without revealing privacy-sensitive parameters from the slice tenants. Moreover, we introduce an adaptive pricing mechanism of the wireless resources to improve the network owner’s profit. We evaluate the performance of our algorithms through simulations and an experimental testbed deployed on the Amazon EC2 cloud, both based on a real-world dataset of base stations from the OpenCellID project. Results conclude that our algorithms converge to the NE rapidly and achieve near-optimal performance, while our pricing mechanism effectively improves the profit of the network owner.

A systematic mixed-integer differential evolution approach for water network operational optimization.

The operational management of potable water distribution networks presents a great challenge to water utilities, as reflected by the complex interplay of a wide range of multidimensional and nonlinear factors across the water value chain including the network physical structure and characteristics, operational requirements, water consumption profiles and the structure of energy tariffs. Nevertheless, both continuous and discrete actuation variables can be involved in governing the water network, which makes optimizing such networks a mixed-integer and highly constrained decision-making problem. As such, there is a need to situate the problem holistically, factoring in multidimensional considerations, with a goal of minimizing water operational costs. This paper, therefore, proposes a systematic optimization methodology for (near) real-time operation of water networks, where the operational strategy can be dynamically updated using a model-based predictive control scheme with little human intervention. The hydraulic model of the network of interest is thereby integrated and successively simulated with different trial strategies as part of the optimization process. A novel adapted mixed-integer differential evolution (DE) algorithm is particularly designed to deal with the discrete-continuous actuation variables involved in the network. Simulation results on a pilot water network confirm the effectiveness of the proposed methodology and the superiority of the proposed mixed-integer DE in comparison with genetic algorithms. It also suggests that 23.69% cost savings can be achieved compared with the water utility's current operational strategy, if adaptive pricing is adopted for all the pumping stations.

A market‐oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage

AbstractIn this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid‐tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time‐varying market‐price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market‐price data, depict the proficiency of the proposed methodology over standard linear programming methods.

Network-wide adaptive tolling for connected and automated vehicles

This article proposes Δ-tolling, a simple adaptive pricing scheme which only requires travel time observations and two tuning parameters. These tolls are applied throughout a road network, and can be updated as frequently as travel time observations are made. Notably, Δ-tolling does not require any details of the traffic flow or travel demand models other than travel time observations, rendering it easy to apply in real-time. The flexibility of this tolling scheme is demonstrated in three specific traffic modeling contexts with varying traffic flow and user behavior assumptions: a day-to-day pricing model using static network equilibrium with link delay functions; a within-day adaptive pricing model using the cell transmission model and dynamic routing of vehicles; and a microsimulation of reservation-based intersection control for connected and autonomous vehicles with myopic routing. In all cases, Δ-tolling produces significant benefits over the no-toll case, measured in terms of average travel time and social welfare, while only requiring two parameters to be tuned. Some optimality results are also given for the special case of the static network equilibrium model with BPR-style delay functions.

Adaptive pricing for optimal resource allocation in industrial production sites

In large integrated production sites, an optimal allocation of the shared resources among different possibly competing production plants is key to a resource and energy efficient operation of the overall site. Typically, a large integrated production site can be regarded as a physically coupled system of systems (SoS), since it comprises many different physically linked production plants with a certain degree of autonomy in respect to the individual operating conditions where the plants tend to pursue their own economic goals and interests. In order to improve the overall operation of the production site, a centralized optimization for the shared resource allocation within the site is favored. However, a centralized solution cannot always be realized due to various technical or managerial reasons. One of the reasons is the limited amount of information about the individual subsystems that a central site management can access, because the subsystems want to preserve a high level of confidentiality. In this contribution, we present the application of price-based coordination (subgradient-based price updates and the Alternating Direction Method of Multipliers, ADMM) to the case study of the integrated petrochemical production site of INEOS in Köln. We discuss the requirements of the price-based coordination for industrial applicability in the case of limited sharing of information. In a simulation study, we show how the central site management uses price incentives to steer the individual productions plants towards a site-optimal operation and thus is able to react to changing conditions such as capacity changes.

Doubly dynamics for multi-modal networks with park-and-ride and adaptive pricing

This paper models and controls a multi-region and multi-modal transportation system, given that the travelers can adjust their mode choices from day to day, and the within-day traffic dynamics in the network also evolve over days. In particular, it considers that the city network can be partitioned into two regions (center and periphery). There are park-and-ride facilities located at the boundary between the city center region and the periphery. Travelers can either drive to the city center, or take public transit, or drive to the park-and-ride facilities and then transfer to the public transit. Travelers can “learn” from their travel experience, as well as real-time information about traffic conditions, thus will adjust their choices accordingly. It follows that the dynamic traffic pattern (within-day) in the city network will evolve over (calendar) time (day-to-day). To improve traffic efficiency in the network, an adaptive mechanism, which does not need detailed travelers’ behavioral characteristics, is developed to update parking pricing (or congestion pricing) from period to period (e.g., one period can be one month). The developed doubly dynamics methodological framework coupled with a feedback pricing mechanism unfolds and influences equilibrium system characteristics that traditional static day-to-day models cannot observe. The proposed adaptive pricing approach is practical for implementation in large-scale networks as the variables involved can be observed in real life with monitoring techniques. Also, it can contribute to reduce total social cost effectively, as shown in the numerical experiments.

Dual bounds of a service level assignment problem with applications to efficient pricing

We extend the classical continuous assignment problem to allow for the assignment of multiple service levels. This formulation encompasses an important class of lead-time allocation problems in queues where congestion may impact feasible assignments of customers to service levels. Through analysis of the dual program we show a number of results which include existence of welfare maximizing prices for a more general set of operational settings than previously reported. Further, we show that the duality gap provides a bound on welfare loss that can be calculated from operational information in a service system with priced service levels. For systems where total expected delay is a convex function of the customer arrival rate, this bound is shown to be an upper bound on the difference between current and maximum welfare and provide a sufficient condition for maximizing welfare. We demonstrate how this bound may be used to evaluate and improve a current set of prices through use as a measure to guide an adaptive pricing algorithm. The adaptive pricing algorithm is shown through computational experiments to find pricing schemes which deliver near optimal welfare.

INDIVIDUAL TYPOLOGICAL FEATURES OF ADAPTATION MECHANISMS AND HEART RATE VARIABILITY IN ROWERS DEPENDING ON THE DIRECTION OF COMPETITIVE ACTIVITY

Objective: to study the individual features of adaptation mechanisms for the direction of the training and competitive process at stages of a year-long training of rowers-canoeists. Material and methods. We have studied the typological features of vegetative regulation in rowers, members of the national kayak-paddling and canoeing rowing team by the method of cardiointervalography with PAK «Omega-C». The features of restoration of adaptation reserves were evaluated with parameters of the spectral analysis of HRV in the preparatory and precompetitive periods during two years of the training at the beginning of the weeklong microcycle after a day of rest. Results. We have identified three types of muscular metabolic activity in the rowers: aerobic «stayer», anaerobic «sprinter» and aerobic-anaerobic, which corresponded to the character of the competitive activities of the sportsmen. The studied rowers revealed individual typological features of autonomic providing of HRV which are implemented by inclusion of different regulatory systems. Conclusion. The level of manifestation of decrease of the parameters of HRV spectral analysis in the sportsmen reflects the physiological price of adaptation to the direction of the competitive activity.

Reactive Pricing: An Adaptive Pricing Policy for Cloud Providers to Maximize Profit

Traditional static pricing strategies are ineffective for cloud providers to maximize their profit since they cannot leverage the supply-and-demand relationship of computing resources hosted in data centers. In this paper, we consider a dynamic server pricing (DSP) problem where the data center operator determines the server price based on the resource demand. In addition, the operator should also take the renewable energy, spot power price, and battery level into account. To solve the DSP problem, we propose a reactive pricing (RP) algorithm which dynamically tunes the server price in response to state change. It is shown through theoretical analysis and real world traces driven simulations that RP achieves a close-to-optimal profit and is robust against exogenous environment variations.

Dynamic Pricing for Cloud Service Negotiation

Big players in cloud service market like Microsoft, Amazon and Google keeps changing their service price. Increasing cloud service offerings have motivated cloud providers to develop cloud service marketplace such as AWS, Google Apps and Cloud Surfing. However, these marketplaces lack dynamic pricing mechanism. The idea of dynamic pricing in the cloud services market has emerged in recent years and there is a need to design and implement an appropriate pricing model for managing the service demand and capacity. This paper reviews dynamic pricing implementation in cloud services and similar areas. This paper reviews recent concurrent cloud SLA negotiation frameworks. A concurrent cloud SLA negotiation mechanism with adaptive pricing is proposed. The proposed mechanism is designed to address multiplicity, information sharing and complexity issues in current frameworks. © 2016 American Scientific Publishers. All rights reserved.

Price-based load coordination revisited: augmenting open-loop coordination approaches

AbstractThe activation of flexible loads through demand side management offers opportunities for more efficient power systems operations. Price-based incentive are a straight-forward form for decentral coordination of these flexible loads. However, their applicability has recently been seen more pessimistic as they may induce new load peaks due to herding effects. We revisit these results by characterizing desynchronized posted pricing approaches. Illustrating highly flexible load by means of electric vehicle charging, we show that these desynchronized rate can mitigate the occurrence of extreme load spikes, improve the utilization of renewable generation and in summary create significant system cost savings. Our results show that simple open-loop pricing can almost match the efficiency of closed-loop adaptive pricing in settings with limited system flexibility. We find that the more renewable generation and flexible load are present in the system, the better more complex pricing schemes fare compared to simple ones. This insight may guide regulators and utilities in establishing more effective pricing schemes in retail electricity markets.