Optimal Service Price Research Articles

Managing on-demand ridesharing operations: Optimal pricing decisions for a ridesharing platform

We investigate an on-demand ridesharing system consisting of a ridesharing platform, multiple drivers, and multiple passengers. We begin by analyzing a choice problem for a passenger who chooses either a ridesharing or taxi service and also studying a choice problem for a driver who decides on whether or not to serve. Then, we obtain the ridesharing platform’s optimal service price charged to passengers and its optimal wage paid to drivers. We perform sensitivity analysis to draw a number of managerial implications. An increase in the number of potential passengers (drivers) usually results in an increase (a decrease) in both the price and wage. We also find that the platform is better off when both the number of passengers and the number of drivers are higher, and an increase in the passengers’ mental costs for the taxi service can help increase the platform’s profit. Moreover, the dynamic pricing strategy can not only improve the platform’s profit but also generate larger surpluses to passengers and drivers.

Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era

On-demand service platforms are popular nowadays. Many platforms hire agents to serve customers who are risk sensitive towards the waiting-time. In this paper, we apply the mean-risk theory to analytically explore how the risk attitude of customers affects the optimal service pricing decision of the on-demand platform, consumer surplus (CS) of customers, the expected profit (EP) and profit risk (PR) of the platform (and the hired service agents). In the basic model, assuming consumers are homogeneous, we find that if the customers are more risk averse (risk seeking), the optimal service price will drop (increase). Comparing among the three different risk attitudes of customers, we find that when the customers are risk seeking, the CS and the platform's EP are highest, even though the platform's PR is also highest. While the opposite happens when the customers are risk averse. In the extended model with a market including customers with different risk attitudes, the blockchain technology helps the platform assess the proportion of risk seeking, risk neutral and risk averse customers accurately. We explore the optimal service prices under both the common pricing policy and the customized pricing policy (with-respect-to customer's risk attitude), and derive the value of blockchain technology mediated customized service pricing strategy. We conclude by highlighting that the risk attitudes of customers play a critical role in determining the optimal on-demand service pricing, and the blockchain technology is a valuable technological tool to help.

Optimal pricing and environmental improvement for a hazardous waste disposal supply chain with emission penalties

Hazardous waste management has become a municipal problem, associated with industrial development and changing consumption patterns. We study the hazardous waste management system as a supply chain with a disposal facility and a contractor, which serves customers with demand sensitive to price and environment impact. The government intervenes with this supply chain by imposing emission penalties that make the waste disposal facility responsible for reducing disposal process emissions. The Stackelberg game and Nash equilibrium are used to model the decision-making sequence between the disposal facility and the contractor. We specify different scenarios to model the hazardous waste management supply chain. The goal of this research is to find the optimal disposal service price and level of environmental improvement to mitigate adverse impacts and maximize profits. The applicability of dual-channel service in the hazardous waste supply chain is also investigated. Our study shows that the supply chain obtains the highest profit if both the disposal facility and the contractor operate in a centralized model. Under some scenarios, profitability improves if the disposal facility becomes active in waste collection and transportation under a dual-channel supply-chain model. Demand sensitivity to price and environmental impacts can affect the adopted level of improvement by disposal facilities.

Optimal Service Pricing and Charging Scheduling of an Electric Vehicle Sharing System

Electric vehicles (EVs) has tiny environmental impact and will constitute a major mean of urban transportation in the future. Shared EV is quickly becoming a new business model under the sharing economy initiatives, providing easy access for commuters who possess no private cars. In this paper, we consider a carsharing company that owns EVs and some parking lots. Passengers can hire an EV at one parking lot and drive it to another one and pay for the service at a certain price determined by the company. A dedicated EV mobility model is proposed to capture the spatial transportation of energy without tracking every single vehicle. Price elasticity is described by a linear demand-price function. The company schedules the aggregated charging of unoccupied EVs in each parking lot, aiming at maximizing the total profit. Parking lots possess relatively large capacity and have to participate in a distribution power market; energy consumption is paid at the locational marginal price. The decision-making problem of the company is formulated as a bilevel program. The lower level simulates the distribution market clearing, and the upper level represents the pricing and charging scheduling problem faced by the company. Starting from a global polyhedral approximation of the power flow model, we develop an equivalent mixed-integer program based on primal-dual optimality condition and integer algebra technique, together with a warm-start strategy which accelerates computation remarkably. Case studies demonstrate that the proposed business model can reshape the load profile by shaving the peaking and filling the valley without harming the profit of the company.

Service strategies and pricing based on heterogeneity of service values and waitingcosts in queuing system

PurposeThis paper aims to study the corresponding optimal service strategies and pricing in the styled non-preemptive M/M/1 service systems from different objectives, which consider both heterogeneous waiting costs of customers and service values in customer segmentation.Design/methodology/approachIn this paper, the authors consider two service situations where customers cannot leave the service system (i.e. monopoly service system) and customers can leave the service system freely (i.e. non-monopoly service system), respectively. The authors study the following four different perspectives that are revenue, social welfare, social cost and utility of customers. The authors first build up a new model, then propose the related objective functions. Further, the authors optimize the corresponding functions and achieve the optimal results. Later, the authors propose the corresponding optimal strategies. Finally, the authors use a practical numerical case to verify the proposed results.FindingsThe results of this paper indicates that the service provider should adopt classification services to gain the maximum revenue, the maximum social welfare and the minimum social costs by charging a priority fee in above two service systems. However, the service provider should cancel customer classification and keep regular customer only to obtain maximum utility. In the monopoly service system, both the optimal proportion priority customers and the revenue decrease with the increasing of the service rate, while in a non-monopoly service system, both of them are increasing with the service rate improving.Originality/valueThis paper first considers both heterogeneity of service values and waiting costs in queuing system, then the author set up a new model based on this assumption. Moreover, the authors draw the corresponding management insights based on the optimal results, which were unavailable before.

Sustainable service design and revenue management for electric tour bus systems: seoul city tour bus service and the eco-mileage program

Sustainable tourism has true sustainability when it is designed economically and in an environmentally friendly fashion. A variety of methods has been explored to achieve sustainable tourism, and the use of new technologies is a significant trend for sustainable tourism. In the City Tour Bus service, an environmentally friendly tour service can be achieved by using electric buses. Moreover, the social energy saving program helps to reduce the economic burden of building eco-friendly services by providing economic incentives as rewards for environmental contributions. Two mathematical models are newly developed to quantitatively design and operate a sustainable City Tour Bus service. Mathematical model I is developed with two objective functions of minimizing total investment cost and CO2 emissions. It derives from Pareto solutions with different number of electric buses and battery capacities. Based on the solutions of mathematical model I and the economic incentive from the social energy saving program, mathematical model II investigates the optimal unit service price. Proposed quantitative approaches are applied to the Seoul City Tour Bus service with Seoul Eco-Mileage Program. These new approaches are expected to build and manage sustainable tourism system in scientific way. Moreover, they will provide quantitative bridge between sustainable transportation system and social programs beyond tourism industry.

Shared Electrical Energy Storage Service Model and Strategy for Apartment-Type Factory Buildings

Electrical energy storage (EES) is a promising and convenient solution for energy efficient buildings, but the high cost of EES limits the expansion of its use. This study presents a shared EES (s-EES) service model including the architecture for implementing the service and a strategy for operating the service for apartment-type factory buildings composed of individually owned units. The proposed architecture for the s-EES service consists of physically connected energy and communication infrastructures, and logical operation of the EES virtually assigned to each participating unit. The conceptual scheme for the s-EES service is presented in the architecture. The service strategy is designed to meet the profit maximization problems of the participants and energy service provider (ESP). The optimal s-EES size and service price conditions are calculated using Lagrangian relaxation, and exchanging information between the participant and the ESP. A case study which uses data from Korea shows that more than 80% of the units in a building participate in the service because of the benefits. The benefits for both the ESP and the units grows with an increase in participating units and a decrease in EES cost. Considering 30%, 50% and 70% EES cost reduction, the ESP's profit increases linearly to about 8, 850, and 1500 dollars a month, respectively. The additional total profit of participants increases exponentially to about 300, 640, 2400 dollars a month, respectively, when compared to individual EES usage. In the s-EES service model, an ESP can provide EES service at a low price through economies of scale, and units in the building can use the EES at a lower price than if they were installed individually.

Strategic Access and Pricing in Internet of Things (IoT) Service With Energy Harvesting

This paper studies an interaction between the Internet of Things (IoT) device and users, both of which act strategically to achieve their own objectives. In the IoT system under consideration, the users access the service provided by the IoT device, e.g., to obtain sensing information. The device relies on energy harvesting to serve the users who are priced when accessing the service. When the delay cost of users is considered, we propose a game-theoretic queuing model to analyze the pricing strategy of the device and strategic joining rules for users. A Stackelberg game is formulated, in which the device, i.e., the leader, determines the service price to maximize his revenue facing the strategic users, i.e., the followers who determine their equilibrium joining strategy to maximize their own utility. Interestingly, we find that the equilibrium joining probability can be non-monotone in the length of energy harvesting time. Moreover, from the perspective of a social planner, the optimal service price to induce the maximal social welfare is derived, and the Price of Anarchy metric is examined accordingly. The numerical examples disclose that the socially optimal price should be lower than the optimal price for the device.

Dynamic pricing and time-to-market strategy in a service supply chain with online direct channels

Abstract Manufacturers’ operation strategies become considerably complicated due to more frequent product updating in the Internet economics. This paper deals with the multi-stage problem of pricing and time-to-market for multi-generation products sold through an online direct channel in a service supply chain with a manufacturer and a logistics service provider. The manufacturer introduces a new generation of products before the present one exits market. The old generation of product may or may not exit the market before the end of the sales period. The service provider is in charge of the logistics service from the manufacturer to the consumers. The manufacturer’s optimal pricing and introduction time strategies and the logistics service provider’s optimal service pricing strategy can be derived. When the substitution rate of a new product to an old one is closer to a threshold, the greater the competition between them, and therefore the lower their optimal prices and the sooner the old product exits market. Moreover, the optimal time-to-market for the new product is decreasing with the potential market share of two generations of products in the second stage. In addition, higher quality which leads to higher costs makes the product enter the market later. Finally, we use numerical examples to illustrate the strategies.

The optimization of pricing strategy for the wind power equipment aftermarket service

PurposeAlthough the wind power industry has been booming in China during the last decade, the development of wind turbine aftermarket service is still lagging behind, which seriously affects the operational efficiency of wind farms. If wind turbine manufacturers get involved in the aftermarket, the service pricing policy will impact the profits of both the manufacturer and the wind farm. Therefore, it is necessary to discuss an optimal service pricing strategy in the wind turbine aftermarket and design a method to improve electricity generation efficiency through service contract design. The paper aims to discuss these issues.Design/methodology/approachIn order to decide the maintenance quantity and channel effort level, the authors design a normal Stackelberg game and an efficiency value-added revenue-sharing contract and discuss two kinds of revenue increment sharing models under situations, in which the supply chain’s leaders are the wind farm and the wind turbine manufacturer, respectively.FindingsThe results show that in either case, there exist optimal power generation revenue-sharing ratios that can maximize profit. At the same time, the authors outline an optimal service pricing policy, maintenance demand policy and channel service effort-level policy. The results summarize the influences of wind aftermarket services on wind farms’ and wind turbine manufacturers’ profit, which provides managerial insights into the process of manufacturing servitization.Practical implicationsThe manufacturer’s channel effort level will influence the power generation increments very much, so the authors have developed a mechanism to stimulate the manufacturer improving the efficiency of aftermarket services.Originality/valueTaking the power generation increment revenue as the profit increment function, the authors discuss the influence of service price on the profit increment of the wind farm and the wind turbine manufacturer and also consider the influence of service price on the wind farms maintenance quantity and wind turbine manufacturers channel effort level.

Profit Maximization Mechanism and Data Management for Data Analytics Services

With the advancement and emergence of new network services, such as social network, Internet of Things, and crowdsensing, large volume of diverse data is collected, shared, and leveraged to develop analytics services. The data analytics service has become a key commodity that can be traded among various economic entities. In this paper, we address the optimal pricing mechanisms and data management for data analytics services and further discuss the perishable services in the time varying environment. We first propose a data market model and define the data utility based on the impact of data size on the performance of data analytics, e.g., prediction and verification accuracy. For perishable services, we study the perishability of data that affects the service quality and provide a quality decay function. The data analytics services are considered as digital goods and uniquely characterized by “unlimited supply” compared to conventional goods. Therefore, we apply the Bayesian profit maximization mechanism in selling data analytics services, which is truthful, rational, and computationally efficient. The optimal service price, data amount, and service update interval are obtained to maximize the profit under different customer’s valuation distributions. Finally, experimental results on real-world datasets show that our data market model and pricing mechanism effectively solve the profit maximization problem and provide useful strategies for the data analytics service provider.

Integrating Distributed Grids With Green Cellular Backhaul: From Competition to Cooperation

This paper considers the problem of renewable energy allocation for a cellular backhaul network where each backhaul node can request renewable energy from a microgrid to forward the traffic. Each microgrid includes multiple renewable power suppliers (RPSs), which directly compete with each other to be the sole supplier of the node. The operator needs to specify the energy storage level for each RPS along the backhaul and the wireless service price for the end users. Then, the RPSs will determine their individual energy prices. The problem is formulated as a Stackelberg game between the leader (the wireless operator) and multiple followers (RPSs). We will identify the RPSs’ unique equilibrium pricing scheme as well as the operator’s optimal energy storage and wireless service price decisions. Our analysis also provides the important observation that direct competition between RPSs on the same backhaul node will induce the operator to store more energy and can thus improve the revenues of both the operator and the RPSs. Furthermore, we consider that RPSs along the backhaul may form coalitions to better coordinate their pricing decisions when they sell energy to the wireless operator. Such a study leads to a hybrid two-level game whose the first level is similar to the Stackelberg game for the non-cooperative scenario. We will show that a coalition formation depends crucially on how the wireless traffic will be affected by the energy price reduction due to the coalition formation. In a nutshell, the proposed techniques provide insights for operating a green cellular backhaul by using the distribute energy sources in a decentralized market setting. Finally, simulation results are presented to compare the solutions of the competitive model with those of the cooperative model. We show that forming coalitions between RPSs benefits both the RPSs and the operator.

Ride Solo or Pool: The Impact of Sharing on Optimal Pricing of Ride-Sharing Services

A ride-sharing platform (RSP), such as Uber or Lyft, can sometimes offer passengers an option to share (pool) the ride with fellow passengers. On the one hand, a passenger who pools benefits from paying a lower fare and the RSP benefits from increasing occupancy per car, thereby serving more passengers. On the other hand, a passenger who pools takes more time, on average, to reach her destination and may have to share the ride with a stranger, and the RSP gets a lower profit margin per passenger than from solo rides. We develop a queueing model to find the RSP's optimal revenue at equilibrium when passengers are strategic, and drivers are independent agents, and design the RSP's revenue-maximizing price-service menu. We find that offering both solo and pooled rides is optimal when the distribution of passenger-type is not skewed, and congestion is not high. Counter intuitively, when congestion is high, the RSP benefits from offering only one ride choice, and simulation-based results extend these findings when more than one route exists. Revenue per driver can be non-monotonic with respect to the number of cars, even though total revenue is monotonic. Numerical analysis provides important insights when the number of drivers is endogenous. For instance, equilibrium revenue per driver can decrease when the passenger arrival rate increases. We find that when the driver supply side thickens, revenue per driver decreases. The compensation drivers receive (as a fraction of total revenue generated) increases with their reservation price and decreases with the arrival rate of passengers. When demand is low, a higher wage-payout fraction can increase RSP's equilibrium revenue.

Optimal Nonlinear Pricing in Social Networks under Asymmetric Network Information

We study the optimal nonlinear pricing of products and services in social networks, where customers are strategic and their consumption exhibits local externality. The stochastic network formation process generates both network uncertainty and information asymmetry: customers know about their local network characteristics but the selling firm only has knowledge of global network. The firm may adopt nonlinear pricing to serve customers with heterogeneous and unobservable network positions. The firm’s profit maximization resembles a principal-agent problem, complicated with the externalities in agents’ payoffs.We develop a novel solution approach using calculus of variations to tackle this non-standard principal-agent problem. We show that the optimal pricing scheme involves either a quantity premium or quantity discount, which is in strict contrast with the linear pricing case. Applying our results to the random graph (a special case of the social network model we use), we find that the pricing scheme should not discriminate by network positions; consequently, the firm can offer a simple uniform price.

Channel coordination through subsidy contract design in the mobile phone industry

Abstracts This paper examines the feasibility of employing subsidy contracts as a control mechanism to optimise the mobile phone sales channel. We investigate a dual-channel that consists of a telecommunication service operator (TSO) and a mobile phone manufacturer (MPM). The MPM׳s optimal production quantity and optimal retail price and the TSO׳s optimal service capacity and optimal service price are derived in both the decentralised and centralised MPSC models. The modelling results show that the coordinated MPSC leads to profit increase for the MPSC as a whole. More importantly, our analysis demonstrates that a properly designed subsidy contract can achieve the channel coordination in the MPSC. However, such channel coordination through subsidy contract is subject to certain conditions in which Pareto improvement can be achieved.

Learning tastes through social interaction

This paper offers an information-based model of social interaction, and analyzes optimal investment and pricing of services that facilitate interaction in a duopoly. Agents have uncertainty over their preferences but are aware that they are correlated with others’, so there exists an incentive to communicate with others in the population. When a firm's good can be bundled with a coordination mechanism for its consumers, its value is endogenously determined due to a consumption externality. Although this mechanism increases total surplus, it is underprovided and consumer surplus decreases.

Price and speed decisions in customer-intensive services with two classes of customers

In many customer-intensive services, the perceived quality of service decreases in the speed of service. Usually, an increase in service speed induces different marginal reductions in quality for heterogeneous customers. To bring insight into the managerial implications of this difference, we classify customers in terms of intensity parameters, and investigate the behavior of each class of customers in a queueing framework. The optimal service speed and price are derived to maximize service provider’s revenue. Our results demonstrate that no class is always attractive to the provider, and thus there are usually several combinations of service price and speed reaching the same maximal revenue. Moreover, under some mild conditions, the provider could gain more revenue by treating different classes with discrimination than by adopting uniform treatment.

Coordination mechanism of IT service supply chain: an economic perspective

Currently, there are two models of service delivery in the information technology (IT) arena: implementation agent and software-as-a-service (SaaS), and each model results in a different IT service supply chain (SSC) structure. When IT service is delivered via implementation agents, independent software vendors (ISV) form the core of the supply chain, while in SaaS chains, platform operators act as the core. It is important to clarify how to coordinate supply chain participants in their respective supply chains, because there is sparse research about supply chain coordination contracts in the IT service industry. This paper draws upon the theory of revenue sharing contracts and investigates IT SSC coordination mechanisms under both SaaS and implementation agent models. The analytical results indicate that a SaaS supply chain has an optimal IT service price that is only related to the variable costs (Cos) of the SaaS platform operator, and so if the variable cost (Cos) can be reduced to zero, perfect coordination would exist. In contrast, the implementation agent supply chain achieves perfect coordination when the variable cost of the implementation agent is equal to the average unit implementation fee. By comparing the two supply chain models and interpreting their respective coordination mechanisms, this study can further advance supply chain contract theory research.

Research on Service Capacity Coordination Strategy in Product Servitization Supply Chain

This paper introduces a three-echelon product Service-oriented supply chain consisting of specialized service function provider, service integrator and customer. Service integrator purchases service capacity from specialized service function provider and sales the added service to customer facing with a price sensitive stochastic demand market. The purpose of service integrator is to meet expected profit maximization through the optimal service capacity order quantity and price of integrated services. Specialized functions service provider identifies the best service capacity price to maximize the expected profit. Establishing the revenue model and supply chain coordination model in product Service-oriented supply chain based on service capacity. Through the numerical example, we find that service capability has an important influence on product Service-oriented supply chain coordination performance.

Optimal Pricing of Services with Switching Costs

Customer switching costs are an important factor in account-based services such as telecommunications, financial, insurance and brokerage services. In these businesses, existing customers incur significant costs if they switch to another provider. Such costs include physical configuration and installation costs, contractual costs (e.g. termination fees) and cognitive costs of learning. These switching costs enable a firm to extract more revenue from incumbent customers by charging them higher prices. However, higher prices disproportionately deter new customers from buying, because, ex ante, they face similar set-up and learning costs with all providers and hence are more price sensitive. This raises an important question of how best to balance the tradeoff between short-term revenue gains and long-run account growth. We develop an optimal control model to study this tradeoff. We show that a simple target market share policy is the optimal strategy. In particular, there exists a target price and target market share such that the firm should price to reach the target market share as fast as possible, at which point it should switch to the target price. We also examine how these targets change with the competitive outside price, shopping frequency of customers, firm’s discount rate and market growth rate. In addition, we extend the basic monopoly model to the duopoly case in which two firms compete for market share and maximize their expected discounted revenue. We also look at the situation in which a firm is able to charge a lower introductory price to attract new customers.