Showing papers on "Dynamic pricing published in 2020"

Operations management in the age of the sharing economy: What is old and what is new?

Abstract: The sharing economy, a term we use to refer to business models built around on-demand access to products and services mediated by online platforms that match many small suppliers or service provide...

Dynamic pricing and matching in ride‐hailing platforms

Abstract: Ride‐hailing platforms such as Uber, Lyft, and DiDi have achieved explosive growth and reshaped urban transportation. The theory and technologies behind these platforms have become one of the most active research topics in the fields of economics, operations research, computer science, and transportation engineering. In particular, advanced matching and dynamic pricing (DP) algorithms—the two key levers in ride‐hailing—have received tremendous attention from the research community and are continuously being designed and implemented at industrial scales by ride‐hailing platforms. We provide a review of matching and DP techniques in ride‐hailing, and show that they are critical for providing an experience with low waiting time for both riders and drivers. Then we link the two levers together by studying a pool‐matching mechanism called dynamic waiting (DW) that varies rider waiting and walking before dispatch, which is inspired by a recent carpooling product Express Pool from Uber. We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, and welfare. We also highlight several key practical challenges and directions of future research from a practitioner's perspective.

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

Abstract: We consider a seller who can dynamically adjust the price of a product at the individual customer level, by utilizing information about customers’ characteristics encoded as a d-dimensional feature vector We assume a personalized demand model, parameters of which depend on s out of the d features The seller initially does not know the relationship between the customer features and the product demand, but learns this through sales observations over a selling horizon of T periods We prove that the seller’s expected regret, ie, the revenue loss against a clairvoyant who knows the underlying demand relationship, is at least of order s √T under any admissible policy We then design a near-optimal pricing policy for a “semi-clairvoyant” seller (who knows which s of the d features are in the demand model) that achieves an expected regret of order s √Tlog T We extend this policy to a more realistic setting where the seller does not know the true demand predictors, and show that this policy has an expected regret of order s √T(log d＋logT), which is also near-optimal Finally, we test our theory on simulated data and on a data set from an online auto loan company in the United States On both data sets, our experimentation-based pricing policy is superior to intuitive and/or widely-practiced customized pricing methods such as myopic pricing and segment-then- optimize policies Furthermore, our policy improves upon the loan company’s historical pricing decisions by 47% in expected revenue over a six-month period

A review of optimal charging strategy for electric vehicles under dynamic pricing schemes in the distribution charging network

Abstract: This study summarizes a critical review on EVs’ optimal charging and scheduling under dynamic pricing schemes. A detailed comparison of these schemes, namely, Real Time Pricing (RTP), Time of Use (ToU), Critical Peak Pricing (CPP), and Peak Time Rebates (PTR), is presented. Globally, the intention is to reduce the carbon emissions (CO2) has motivated the extensive practice of Electric Vehicles (EVs). The uncoordinated charging and uncontrolled integration however of EVs to the distribution network deteriorates the system performance in terms of power quality issues. Therefore, the EVs’ charging activity can be coordinated by dynamic electricity pricing, which can influence the charging activities of the EVs customers by offering flexible pricing at different demands. Recently, with developments in technology and control schemes, the RTP scheme offers more promise compared to the other types of tariff because of the greater flexibility for EVs’ customers to adjust their demands. It however involves higher degree of billing instability, which may influence the customer’s confidence. In addition, the RTP scheme needs a robust intelligent automation system to improve the customer’s feedback to time varying prices. In addition, the review covers the main optimization methods employed in a dynamic pricing environment to achieve objectives such as power loss and electricity cost minimization, peak load reduction, voltage regulation, distribution infrastructure overloading minimization, etc.

Feature-Based Dynamic Pricing

Abstract: We consider the problem faced by a firm that receives highly differentiated products in an online fashion. The firm needs to price these products to sell them to its customer base. Products are des...

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

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

Dynamic pricing in the presence of reference price effect and consumer strategic behaviour

Abstract: This paper presents a model for designing two-stage dynamic pricing strategies when the seller faces strategic consumers in the presence of a reference price effect. The consumers form the utilitie...

ROD-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-on-Demand Service Using Multi-Source Urban Data

Abstract: Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand, and to improve service responsiveness and quality. Despite this, on the drivers’ side, dynamic pricing creates a new problem: how to seek for passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. In this paper, we develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies, and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product-form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability–to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers’ revenue. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers’ revenue by offering useful guidance.

Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

Abstract: With the advent of advancements in the power sector, various new methods have been devised to meet modern society’s electricity needs. To cope with these large sets of electronic device’s current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are two-fold; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly, at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.

Three Dynamic Pricing Schemes for Resource Allocation of Edge Computing for IoT Environment

Abstract: With the widespread use of Internet of Things (IoT), edge computing has recently emerged as a promising technology to tackle low-latency and security issues with personal IoT data. In this regard, many works have been concerned with computing resource allocation of the edge computing server, and some studies have conducted to the pricing schemes for resource allocation additionally. However, few works have attempted to address the comparison among various kinds of pricing schemes. In addition, some schemes have their limitations such as fairness issues on differentiated pricing schemes. To tackle these limitations, this article considered three dynamic pricing mechanisms for resource allocation of edge computing for the IoT environment with a comparative analysis: BID-proportional allocation mechanism (BID-PRAM), uniform pricing mechanism (UNI-PRIM), and fairness-seeking differentiated pricing mechanism (FAID-PRIM). BID-PRAM is newly proposed to overcome the limitation of the auction-based pricing scheme; UNI-PRIM is a basic uniform pricing scheme; FAID-PRIM is newly proposed to tackle the fairness issues of the differentiated pricing scheme. BID-PRAM is formulated as a noncooperative game. UNI-PIM and FAID-PRIM are formulated as a single-leader–multiple-followers Stackelberg game. In each mechanism, the Nash equilibrium (NE) or Stackelberg equilibrium (SE) solution is given with the proof of existence and uniqueness. Numerical results validate the proposed theorems and present a comparative analysis of three mechanisms. Through these analyses, the advantages and disadvantages of each model are identified, to give edge computing service providers guidance on various kinds of pricing schemes.

Peer-to-Peer Operation Strategy of PV Equipped Office Buildings and Charging Stations Considering Electric Vehicle Energy Pricing

Abstract: In this article, an operational framework is proposed for peer-to-peer (P2P) energy trading between a group of electric vehicles (EVs) within a charging station and a business entity equipped with solar generation that will significantly improve the benefit of all parties compared to having sole agreements with the utility. A dynamic pricing mechanism for the EVs is developed based on the price of the stored energy that not only increases owners’ profit but also promotes the contribution of charging stations in P2P energy markets. To evaluate the proposed framework, the performance of the system under P2P energy sharing is compared with peer-to-grid (P2G) energy trading. The results show a 23.24% reduction in total cost of prosumers and an improvement of 10% in self-consumption of Photovoltaics (PV) generation as well as 100% participation willingness of prosumers. The results also suggest that considering the availability of solar energy during daylight hours and the possibility to trade the stored energy in EVs outside of home, P2P energy transactions under the proposed control framework will bring significant economic benefits for EV owners.

Peer-to-Peer Energy Sharing in Distribution Networks With Multiple Sharing Regions

Abstract: Peer-to-peer energy sharing in the distribution networks (DN) is an emerging issue with the large-scale development of photovoltaic (PV) prosumers. The DN can be classified into energy-shared regions (ESR) to enable the zonal energy trading. A Stackelberg-game-based energy-sharing framework is recommended for DN with multi-ESR, where the energy-sharing provider (ESP) works as a leader with dynamic pricing for multi-ESR, whereas PV prosumers serve as followers with the demand response's (DR) ability to choose an ESR to link and modify their flexible loads. A profit maximization model, along with multi-ESR pricing and a network usage fee, is designed for the ESP operation in this article. This involves a utility model with DR strategies, including ESR selection and load adjustment, which is proposed for the prosumers. Moreover, the presence and uniqueness of the Stackelberg equilibrium are being provided. Finally, through the use of a real system, the simulation results show that the ESP profit and prosumers can be increased whereas the impact of PV uncertainty and variability on the utility grid is reduced.

Dynamic Pricing and Routing for Same-Day Delivery

Abstract: An increasing number of e-commerce retailers offers same-day delivery. To deliver the ordered goods, providers dynamically dispatch a fleet of vehicles transporting the goods from the warehouse to ...

The Long-term and Spillover Effects of Price Promotions on Retailing Platforms: Evidence from a Large Randomized Experiment on Alibaba

Abstract: Dynamic pricing through price promotions has been widely used by online retailers. We study how a promotion strategy, one that offers customers a discount for products in their shopping cart, affec...

An Overview of Demand Response in Smart Grid and Optimization Techniques for Efficient Residential Appliance Scheduling Problem

Abstract: Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance scheduling problem (RASP) is the problem of scheduling appliances at appropriate periods considering the pricing schemes. The objectives of RASP are to minimize electricity cost (EC) of users, minimize the peak-to-average ratio (PAR), and improve the user satisfaction (US) level by minimizing waiting times for the appliances. Various methods have been studied for energy management in residential sectors which encourage the users to schedule their appliances efficiently. This paper aims to give an overview of optimization techniques for residential appliance scheduling. The reviewed studies are classified into classical techniques, heuristic approaches, and meta-heuristic algorithms. Based on this overview, the future research directions are proposed.

Optimal Dynamic Pricing for Trading-Off User Utility and Operator Profit in Smart Grid

Abstract: A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.

Trade‐in for Cash or for Upgrade? Dynamic Pricing with Customer Choice

Abstract: Trade‐in programs for electronics products, e.g., mobile phones, have been increasingly popular. These programs target at customers (we call them “bargainers”) who seek to salvage or upgrade their old devices. There are two widely adopted trade‐in options: trade‐in‐for‐upgrade and trade‐in‐for‐cash. In this study, we consider a firm who offers both trade‐in options, that is, a hybrid trade‐in program, to acquire old products, then refurbishes and resells them, together with new product over a finite selling horizon. The bargainers choose which option to trade in their products while new customers decide whether to buy a new product or a refurbished one. When the selling price of new product is exogenous, we derive the optimal trade‐in prices of old product and resale price of refurbished product. We show that the optimal trade‐in and resale policies are of a threshold‐type and trade‐in‐for‐upgrade should be offered with a premium refund (compared to trade‐in‐for‐cash) only in early periods of the selling horizon. We further consider two variants of the above base model. In the first extension, the new product has a fixed amount of initial inventory and is not replenishable during the selling horizon. In the second extension, the new product price can also be determined by the firm. Our numerical results demonstrate that the hybrid trade‐in program could generate significantly more profit than either upgrade‐only or cash‐only trade‐in program.

A special price just for you: effects of personalized dynamic pricing on consumer fairness perceptions

Abstract: Personalized dynamic pricing (PDP) involves dynamically setting individual-consumer prices for the same product or service according to consumer-identifying information. Despite its profitability, this pricing provokes strong negative fairness perceptions, explaining why managers are reluctant to implement it. This research provides important insights into the effect of two PDP dimensions (price individualization level and segmentation base) on fairness perceptions and the moderating role of privacy concerns. The results of two experimental studies indicate that consumers perceive individual prices as less fair than segment prices. They also evaluate location-based pricing as less fair than purchase history-based pricing. Consumer privacy concerns moderate these effects.

Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles

Abstract: Autonomous vehicles (AV) create new opportunities to traffic planners and policy-makers. In the case of shared autonomous vehicles (SAVs), dynamic pricing, vehicle routing and dispatch strategies may aim for the maximization of the overall system welfare instead of the operator’s profit. In this study, an existing congestion pricing methodology is applied to the SAV transport mode. On the SAV operator’s side, the routing- and dispatch-relevant cost are extended by the time and link-specific congestion charge. On the users’ side, the congestion costs are added to the fare. Simulation experiments are carried out for Berlin, Germany in order to investigate the impact of SAVs and different pricing setups on the transport system. For the pricing setup, where SAV users only pay the base fare and there is no congestion charge added to the user costs, the model predicts an SAV share of 17.7% within the inner-city Berlin service area. The level of traffic congestion increases, air pollution levels decrease and noise levels slightly increase in the inner-city area. The SAV congestion charge pushes users from SAVs to the walk, bicycle and conventional (driver-controlled) private car (CC) mode. The latter effect is avoided by applying the same congestion charge also to CC users. Overall, this study highlights the importance to control both, the SAV and CC mode in order to improve a city’s transport system.

Dynamic Pricing Mechanism With the Integration of Renewable Energy Source in Smart Grid

Abstract: Day-ahead electricity pricing is an important strategy for electricity providers to improve grid stability through load scheduling. In this paper, we investigate a general framework for modelling electricity retail pricing based on load demand and market price information. Without any a priori knowledge, we have considered a finite time approach with dynamic system inputs. Our objective is to minimize the average system cost and rebound peaks through energy procurement price, load scheduling and renewable energy source (RES) integration. Initially, the energy consumption cost is calculated based on market clearing price and scheduled load. Then, through reformulation and subsequent modification of optimization problem, we utilize a day-ahead price information to construct individualized price profiles for each user, respectively. To analyse the applicability of proposed pricing policy, analytical solution is obtained which is further validated through comparison with solution obtained from genetic algorithm (GA). From results, it is observed that proposed price policy is non-discriminatory in nature and each user obtained a fair electricity tariff rather than a day-ahead price, which is based on load demand and consumption variation of other users. We also show that optimization problem is sequentially solved with bounded performance guarantee and asymptotic optimality. Finally, simulations are carried in different scenarios; aggregated load and market price, and aggregated load, individualized load, market price and proposed price. Results reveal that our proposed mechanism can charge the price to each user with 23.77% decrease or 5.12% increase based on system requirements.

Channel Access Optimization with Adaptive Congestion Pricing for Cognitive Vehicular Networks: An Evolutionary Game Approach

Abstract: Cognitive radio-enabled vehicular nodes as unlicensed users can competitively and opportunistically access the radio spectrum provided by a licensed provider and simultaneously use a dedicated channel for vehicular communications. In such cognitive vehicular networks, channel access optimization plays a key role in making the most of the spectrum resources. In this paper, we present the competition among self-interest-driven vehicular nodes as an evolutionary game and study fundamental properties of the Nash equilibrium and the evolutionary stability. To deal with the inefficiency of the Nash equilibrium, we design a delayed pricing mechanism and propose a discretized replicator dynamics with this pricing mechanism. The strategy adaptation and the channel pricing can be performed in an asynchronous manner, such that vehicular users can obtain the knowledge of the channel prices prior to actually making access decisions. We prove that the Nash equilibrium of the proposed evolutionary dynamics is evolutionary stable and coincides with the social optimum. Besides, performance comparison is also carried out in different environments to demonstrate the effectiveness and advantages of our method over the distributed multi-agent reinforcement learning scheme in current literature in terms of the system convergence, stability and adaptability.