Optimal Pricing Research Articles

Differentiated health insurance policy for coordinating healthcare referral in hierarchical healthcare systems with an internet diagnosis platform

The integration of the Internet with traditional medical services is poised to transform health insurance policies. This study aims to explore the coordinating role of differentiated health insurance policies within the context of Internet healthcare. A four-stage sequential game decision-making model is developed within a queuing framework to address scenarios involving online patient referrals and misdiagnoses. The model begins by analyzing the equilibrium arrival strategy of patients, followed by the determination of optimal service capacity strategies for a nonprofit community health center (CHC) and optimal pricing strategies for a for-profit general hospital (GH). Additionally, the model describes an optimal differentiated subsidy strategy for the government aimed at minimizing total social costs. Analysis reveals that under certain conditions, an increase in the service price at GH relative to CHC can lead to a higher influx of online patients visiting GH in person. Furthermore, when the number of online patients exceeds a specific threshold, it not only prompts the government to increase the disparity in health insurance subsidies between the two hospital tiers but also encourages GH to reduce its service prices and offer free services to online patients. Numerical experiments explore the effects of government budgets, sharing ratios, and other variables on the system's equilibrium state, providing several managerial insights. Notably, when patients' misdiagnosis costs are partially covered, increasing GH's misdiagnosis cost-sharing ratio not only enhances the patient arrival rate but also enhances GH's profitability.

The impact of green innovations on firm’s sustainable operations: Process innovation and recycling innovation

As the urgency and importance of global carbon emission reduction have escalated in recent years, numerous firms are considering ideas to develop innovative green technologies in order to contribute to the low-carbon economy. This paper examines two types of green innovations adopted by firms, namely process innovation (improving production processes to enhance energy efficiency) and recycling innovation (establishing a circular economy to recycle and re-manufacture waste products). By developing a game-theoretical model, our analytical results reveal the following findings. First, we find that both process and recycling innovations can attract more consumers and improve the firm’s profitability under specific conditions. Second, when the cost advantage of remanufacturing is sufficiently low, the adoption of process innovation is always more profitable than recycling innovation. Otherwise, the profitability largely depends on the trade-off between consumers’ perception of the two green innovations. Third, compared to a single innovation, the adoption of both process and recycling innovations (dual green innovation) leads to a further increase in consumer demand and firm’s profit when the cost advantage of remanufacturing exceeds a certain threshold. Furthermore, the optimal pricing set by the firm and consumer demand become more sensitive to consumers’ perception of greenness when dual green innovation is adopted. Finally, the equilibrium result suggests the existence of a “synergistic promoting effect” when the firm implements dual green innovation, which indicates that the advantages of each innovation are amplified when the firm adopts dual green innovation. These results can serve as guidelines for firms aiming to utilize green innovations in order to reduce carbon emissions.

Pricing Decisions in Dual-Channel Supply Chains Considering the Offline Channel Preference and Service Level

With the rapid development of e-commerce, the online channels encroaching on the offline sales market are becoming more serious, which will definitely harm the offline market. Moreover, there exists a certain percentage of consumers (mostly elderly people) who are not able to purchase online because they lack digital skills. Therefore, understanding the impact of the purchase channel preference and service level on pricing decisions is vital for the dual-channel supply chain management. Focusing on the channel preference and service level, we first develop an optimal pricing model containing centralized and decentralized decision-making for an online and offline retailer by deploying the Stackelberg game. We first develop a Stackleberg game to capture such a dual-channel supply chain with the offline channel preference and service level. Secondly, under centralized decision-making, we derive the optimal retail prices and obtain the optimal total profit. Thirdly, under decentralized decision-making, we obtain the optimal retail prices and optimal total profit as well. Moreover, extensive monotonicity properties when system parameters change are obtained. Relying on the theoretical results, firstly, we show that the improvement of the offline service level would lead to higher pricing of the commodities for both online and offline channels. From our numerical results, when the service level is improved, the offline and online optimal pricing increases by 47.5% and 31.1%, respectively, which may contradict the conventional belief that the improvement of one channel would harm another one. Secondly, we demonstrate that the benefit of improving the offline service level has a diminishing marginal effect. The numerical results show that when the current service level is low, the effectives of improving the service level is roughly five times that when the service level is high. This indicates that the investment in improving the offline service level should not be unlimited. Thirdly, we show that the pricing decision under centralized decision-making should be adopted with the existence of both the offline channel preference and offline service.

Dynamic pricing for perishable product in the presence of low-carbon preference

ABSTRACT As countries worldwide strive to implement energy conservation and emission reduction measures, modeling consumers’ low-carbon preferences and purchasing decisions has become a crucial factor in implementing dynamic pricing strategies for firms. This paper investigates the dynamic pricing problems for a single perishable product and substitutable perishable products, respectively, where consumer is affected by low-carbon preference. We first propose the decision framework of consumer choice with low-carbon preference based on the multinomial logit model. On this basis, we establish dynamic pricing models by the dynamic programming method and obtain the optimal pricing strategies. We analytically show that consumer’s low-carbon preference has a positive effect on the optimal price for a single perishable product. Meanwhile, numerical examples illustrate that consumer’s low-carbon preference has a positive effect on the optimal prices for substitutable perishable products. Finally, we give some suggestions to increase firm’s revenue when consumer’s low-carbon preference is present.

Potential and challenges of Peer-To-Peer (P2P) electrical energy trading: investigation and case study

This paper explores the potential and challenges associated with Peer-to-Peer (P2P) electrical energy trading, focusing on both theoretical investigation and practical application through a comprehensive case study. By examining the interactions between prosumers (those who both produce and consume energy) and consumers, we employ utility functions to model these interactions. Optimization is achieved using a non-cooperative game theory approach coupled with real-time pricing mechanisms to reflect market dynamics accurately. Our analysis ensures feasibility and reliability by considering crucial factors such as energy balance and grid capacity constraints, which are essential for maintaining system stability. The simulation results demonstrate the effectiveness of our proposed method. These results highlight the significant potential of P2P trading to enhance overall energy efficiency, promote decentralized energy management, and reduce reliance on traditional energy sources. Furthermore, this study provides detailed insights into maximizing total utility by determining optimal prices and energy trades, ultimately showcasing the transformative impact of P2P trading on real-time energy market operations.

Get by how much you pay: A novel data pricing scheme for data trading

As a crucial step in promoting data sharing, data trading can stimulate the development of the data economy. However, the current data trading market primarily focuses on satisfying data owners' interests, overlooking the demands of data requesters. Ignoring the demands of data requesters may lead to a loss of market competitiveness, customer loss, and missed business opportunities while damaging reputation and innovation capabilities. Therefore, in this paper, we introduce a novel pricing mechanism named Get By How Much You Pay (GHMP) based on compressed sensing technology and game theory to address pricing problems according to data requesters' demands. This scheme employs a dictionary matrix as the sparse basis matrix in compressed sensing. The quality of this matrix directly affects the precision with which the requester can reconstruct the data. If the requester requires higher-precision data, the corresponding payment will also increase accordingly so as to realize the pricing method based on the requester's demands. A game pricing method is proposed to address the final pricing and purchasing issues between the data requester and the data owner by utilizing an authorized smart contract as an intermediary. As an entity participating in the game, the smart contract can only receive a higher transaction fee if it successfully assists the data requester and data owner in completing the pricing. Therefore, it strives to establish more reasonable prices for both parties during the trading process to obtain profits. The experimental results demonstrate that this game-based approach assists the data requester and owner in achieving optimal data pricing, thereby satisfying the maximization of interests for both parties.

Peer‐to‐peer electricity trading via a bi‐level optimization approach for renewable energy‐driven microgrids connected to the distribution grid

AbstractThis study employs a sophisticated bi‐level optimization methodology to model the most efficient operation of microgrids (MGs) within the operational framework of distribution companies (DCs). In this bi‐level optimization problem, the upper level strives to maximize the profits of both MGs owners and DCs, while the lower level is dedicated to ensuring load balance, managing distributed generation, and implementing load curtailment strategies. The coordination of power transmission is facilitated by the DCs. At the upper level of decision‐making, the optimal pricing strategies for power transactions are determined, accounting for various factors such as market prices, demand response programs, and uncertainties in wind speed. Through the utilization of a bi‐level optimization framework, this study comprehensively captures the complex interactions between MGs and DCs, taking into consideration the objectives and constraints of both entities. This approach offers a more precise representation of the decision‐making process in retail electricity markets, thereby providing valuable insights into the optimal operation of MGs within the DCs setting.

Optimal premium pricing in a competitive stochastic insurance market with incomplete information: A Bayesian game-theoretic approach

This paper examines a stochastic one-period insurance market with incomplete information. The aggregate amount of claims follows a compound Poisson distribution. Insurers are assumed to be exponential utility maximizers, with their degree of risk aversion forming their private information. A premium strategy is defined as a mapping between risk-aversion types and premium rates. The optimal premium strategies are denoted by the pure-strategy Bayesian Nash equilibrium, whose existence and uniqueness are demonstrated under specific conditions on the insurer-specific demand functions. Boundary and monotonicity properties for equilibrium premium strategies are derived.

Differential games in the supply chain of innovative products with consumer purchase regret induced returns.

Innovative products entering the market will cause dynamic changes in market demand, and consumers' purchase regret and their return behavior make the market environment more and more complex, which in turn affects the dynamic decision-making in the supply chain. In this paper, under the situation of discrete decision time, combining with the objective reality, we make discrete modification to the classical Bass diffusion model (Bass model), construct a manufacturer-led, retailer-followed supply chain differential game model, analyze the optimal decision-making of the manufacturer and the retailer by combining with the theory of discrete optimal control, and then verify the conclusions by numerical simulation. The results show that: when retailers purchase directly from the manufacturer and sell in the market, the optimal pricing of the innovative product can make the supply chain as a whole, realizing Pareto optimality; consumer's purchase regret will increase the amount of returns, which will lead to the decrease of product sales and the profits of the manufacturer and the retailer; when the innovative product accounts for a different share of the market, the impact of purchase regret on the wholesale price and the retail price are also different. Therefore, manufacturers need to have an extensive comprehension of the market to minimize the negative effects of consumer regret and returns, and to formulate a reasonable pricing strategy for their products to gain as much profit as possible.

Adjustment strategies and chaos in duopoly supply chains: The impacts of carbon trading markets and emission reduction policies

Low-carbon policies serve as a core incentive mechanism to promote carbon reduction in duopoly supply chains. However, their directive impact may cause product prices and carbon emission adjustment speeds to accelerate too quickly, thereby triggering market instability. Current research is noticeably lacking in discussions on optimal configurations of supply chain management strategies and policy implementation under market equilibrium conditions. Therefore, this paper utilizes game theory and chaos theory to explore the dynamic operational strategies and optimal profits of duopoly supply chains under a single carbon trading mechanism and considering carbon reduction incentives. The findings indicate: (1) Price adjustment parameters significantly affect both parties' equilibrium strategies and system stability; (2) An increase in carbon prices and carbon reduction incentive intensity both narrow the stable areas for pricing and reduction strategies; (3) Both manufacturers and remanufacturers experience higher optimal pricing, reduced carbon reductions on their part, and more carbon reductions on the part of their counterparts due to the escalating costs of carbon reduction; (4) While enhancing carbon reduction incentives significantly aids both parties in achieving carbon reductions, as the reduction effect gradually diminishes, it also compresses their profit margins, whereas an increase in carbon quotas can linearly enhance business profits.

Sales mode selection considering the consumer reference quality effect

AbstractThis work investigates a two‐tier supply chain in which a supplier adjusts product quality and prices and distributes the products through a retailer or directly to consumers with the reference quality (RQ) effect. The supplier can choose from three, that is, traditional sales (TS), dual‐channel (DC), and online‐to‐offline (O2O), sales modes. In the O2O sales mode, the retailer is allowed to open an online channel. The market demands are determined by the consumer utility consisting of two parts, a part depending on the consumer RQ effect and the other depending on the retail price and product quality. The profit maximization, that is, the Stackelberg game, models involving the consumer RQ effect are developed using prospect theory, wherein the supplier has the first mover advantage. The analytical optimal solutions are obtained through backward induction. The optimal quality and pricing decisions and the optimal profits of the supply chain members are compared among the three sales modes. A practical application is analyzed to verify the theoretical results of the proposed models. The results show that the consumer RQ effect benefits the two supply chain members under certain conditions. The optimal quality and pricing decisions are significantly affected by the consumer RQ effect except those of the supplier under the O2O sales mode. When consumers have the RQ effect, the DC sales mode is the best choice for the supplier, is a win–win mode for the two supply chain members under certain conditions, and benefits the consumers the most among the three sales modes.

Quality differentiation and optimal pricing strategy in multi-sided markets

This paper analyzes the generalized quality differentiation model in multi-sided markets with positive externalities, which leads to new insights into the optimal pricing structure of the firm. We find that quality differentiation for buyers affects not only the side involving differentiation but also the other side due to cross-side network externalities, thereby affecting the pricing structure of multi-sided firms. In addition, quality differentiation affects the strategic relationships among all the choice variables for the platform, enabling the platform to strategically use quality differentiation to increase its profits.

Impacts of carbon emissions allowance limitations on carbon price with power generation rights trading in China

Carbon pricing is a crucial mechanism for achieving carbon peaking and carbon neutrality through the carbon trading market. Recently, the price of carbon emissions allowances in China's national carbon market has shown a gradual upward trend. The limitations on carbon emissions allowances have also become increasingly stringent. Power generators, which primarily rely on thermal power generation, have a substantial demand for carbon emissions allowances. The rising prices and stricter carbon emissions constraints have increased operational costs. Therefore, exploring the carbon pricing mechanism under more stringent carbon emissions allowance constraints is vital. It encourages thermal power generators to engage in carbon trading and reduce carbon emissions. This paper focuses on an energy trading platform supply chain comprising three power generators, one energy trading platform, and one power retailer. We consider stringent carbon emissions constraints and power generation rights trading. Through the analysis of three scenarios, the study explores optimal pricing decisions for carbon emissions allowances to facilitate carbon emission reduction and promote renewable energy generation and consumption. The findings indicate that, under certain conditions (i.e., the carbon quotas allocation coefficient b1∈[0.73,0.8]), scenario 3, which excludes generation rights trading, yields the highest optimal carbon price. The energy trading platform can adjust various commissions to influence the carbon price, as well as the price and quantity of generation rights trading. Introducing power generation rights trading proves beneficial in reducing carbon emissions (nearly 400 tons less than that in scenario 3 without generation rights trading, when the realized total power demand is around 8600 MWh) and fostering renewable power generation and consumption (the actual renewable power consumption weight is 3% higher than that without generation rights trading). When considering generation rights trading, the high-emission generator and renewable power generator achieve the highest power profits, while for the low-emission generator, the profit in this case is the second best among these scenarios. Moreover, by decreasing the coefficient of carbon quotas allocation and increasing the reduction rate simultaneously, the carbon cost will pass through to the electricity price more sufficiently (e.g., when b1=0.76, the pass-through rate increases from 0.5015 to 0.7984), particularly within a specific range of the carbon quotas allocation coefficient.

Advancing urban electric vehicle charging stations: AI-driven day-ahead optimization of pricing and Nudge strategies utilizing multi-agent deep reinforcement learning

Public charging stations (CSs) serve for electric vehicles (EVs) to charge during urban travel. Optimizing the charging time, location distribution, and power of EVs can increase the revenue of charging system operators (CSOs) and provide flexible regulation resources for the power grid. However, the optimization scheduling of CSs involves the charging choices of various users, which are influenced by their autonomy and bounded rationality. To guide users and encourage their participation in the charging schedule, we introduce the Nudge method from behavioral economics. To achieve collaborative optimization of non-economic Nudges and economic incentive strategies applying to multiple charging stations in a complex nonlinear environment involving users, CSO, and the transportation network, we leverage multi-agent deep reinforcement learning (MADRL). We construct a simulation environment using historical and survey data tailored to real users. This environment facilitates the training of agent groups to enhance decision-making processes. Case studies in a metropolis demonstrate that the agent group aimed at revenue improvement yields significant improvements in the CSO's revenue compared to fixed service fees and pricing strategies without Nudges. Moreover, the agent group aimed at power curve tracking achieves a lower average relative error in aligning the total charging power with the desired curve of the power system. This paper integrates sociological methods into the optimization of physical systems by MADRL, providing a new approach for the scheduling of EV charging considering user behavior.

EXPRESS: The Heterogeneity of Hotel Demand Curves across Consumers and Contexts

This study constructs hotel demand curves at the disaggregate level to uncover the heterogeneity of demand curves across consumers and during both normal periods and times of crisis, exemplified by the pandemic. The novel demand modeling technique fits nonlinear demand curves, parameterizes elasticity dynamics, and enables the comparison of demand curves by essential value. The demand curves for three hotel types in normal and pandemic situations are fitted and decomposed by consumers’ sociodemographics, preferences, and risk tolerance. A pandemic makes the demand curve for midscale [upscale] hotels more inelastic [elastic] and mitigates [amplifies] the influence of individual differences on the demand curve, whereas the demand curve for economy hotels is unaffected by the pandemic. The findings offer insights into the business operations of different hotels, including optimal pricing, customized marketing across consumer segments, and business strategies in case of a health crisis.

Online Retailers based on Different Consumer Types Discount Pricing Strategy Research

In recent years online, retailers have continuously conducted discount sales to stimulate consumers' shopping desire. In this paper, we consider the different responses of three types of consumers: short-sighted, strategic, and discount-seeking consumers to the sales discount period, construct a Steinberg model for e-commerce platforms and online retailers with discount sales periods, and analyze the optimal pricing strategies of different consumer types for e-commerce platforms and merchants. Starting from consumer utility and combining optimization and game theory, it seeks to provide a reference for merchants and e-commerce platforms to make decisions.

Two-dimensional warranty policy design with the consideration of protection term

In practice, car products are sold with 2-dimensional (2D) warranty policies, on which existing literature usually assumes that the products are repaired upon failures throughout the warranty period. However, this is not always the case in real life; for example, car manufacturers may need to replace defective cars during an early period of warranty, when the total time on repairing the failures reaches a specified repair time constraint or the number of repair attempts reaches a specified repair number constraint. Motivated by this, this paper divides a warranty period into two stages: the initial protection term and the subsequent repair stage. During the initial protection term, the manufacturer needs to provide a replacement service when a specified replacement condition is satisfied; while during the subsequent repair stage, the manufacturer should provide repair services upon product failures. Taking the warranty servicing cost into account, a profit model is built. The expected total profit of the product is derived, and the optimal warranty period and product price are designed to maximize the manufacturer’s profit. Finally, numerical experiments on car examples are provided to illustrate the considered warranty policy with protection term.

Capstone Project Data Analytics Untuk Menigkatkan Penjualan Secara Efektif

Amidst the fierce competition in the coffee shop industry in New York, many businesses have experienced a decline in sales, especially in certain products. This research aims to discuss the Capstone Data Analytics project to increase product sales at coffee shops in New York, focusing on the decline in sales of packaged chocolate products. Through a descriptive quantitative analysis approach, sales data from three branches of the coffee shop were evaluated to understand the factors influencing the sales decline. Findings showed that high prices and stagnant sales trends were the main causes of sales decline. Recommendations include price optimization, increased promotion, product diversification, and understanding market trends to increase sales by 2-5% in the following year.

Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization

Deep learning-driven intelligent pricing model in retail: from sales forecasting to dynamic price optimization

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects

Stochastic optimal pricing for retail electricity considering demand response, renewable energy sources and environmental effects