Optimal Pricing Strategy Research Articles

Electric vehicle battery closed-loop supply chain pricing and carbon reduction decisions under the carbon cap-and-trade and reward-penalty policies

Recycling end-of-life electric vehicles (EVs) batteries to conserve resources and reduce carbon emissions has obtained a great deal of concern. This paper studied how carbon cap-and-trade and reward-penalty measures jointly impacted EV battery pricing and decarbonization strategies. Three recycling modes covering single-participator, mixed-participator, and joint recycling are established. Optimal pricing and carbon mitigation strategies, total revenue, and recycling percentage are solved and compared. The dynamic effects of target recycling percentage and rewards and punishments on total revenue and recycling percentage are analyzed by numerical examples. Results show that: (1) The factory price, selling price, collection price, and carbon emission mitigation scale of power batteries are affected by cap-and-trade and reward-penalty mechanisms; (2) Reward-penalty can improve both total revenue and recycling percentage; (3) The cap-and-trade mechanism optimizes the total revenues, showing that the total revenue increase with the increasement of carbon quota and carbon price, while the increase of carbon price leads to worse recycling percentage; (4) The supply chain performance under different recycling modes is affected by policy intervention, and the joint recycling mode is better.

Optimal pricing scheme for addictive goods

AbstractThis article analyses how consumers' habit formation and addiction affect firms' pricing policies. I consider both sophisticated consumers, who realize that their current consumption will affect future tastes, and “naive” consumers, who do not. The optimal contract for sophisticated consumers is a two‐part tariff. The main result is that the optimal pricing pattern when the consumer is naive is a “bargain then rip‐off” contract, namely a fixed fee, with the first units priced below cost, and then priced above marginal cost. This holds both under symmetric and asymmetric information about the consumers' degree of sophistication.

A Multi-Model Output Fusion Strategy Based on Various Machine Learning Techniques for Product Price Prediction

In the digital era, precise product price prediction becomes crucial for enhancing competitiveness in the online marketplace. This paper presents a hybrid model framework that enhances the accuracy of online product price predictions by integrating several machine learning algorithms, including Linear Regression, Decision Trees, and Gradient Boosting. The objective of this approach is to leverage the distinct advantages of each model to address their individual limitations and create a robust unified predictive model. This integration allows for improved handling of complex data relationships and diverse market dynamics that are typical in online sales environments. The results demonstrate that the hybrid model achieves superior prediction accuracy, as reflected in reduced Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics, and an exceptionally high R ² value compared to single-model approaches. These outcomes underscore the efficacy of combining multiple predictive models to enhance the precision of price forecasts in the highly competitive online marketplace. This model fusion strategy not only provides more accurate pricing predictions but also offers strategic insights into the optimal pricing strategies for businesses looking to enhance their market position.

The Art of Balancing Price and Plug: Developing a Theoretical Model for Dynamic Pricing in the Electric Vehicle Market

This study presents a novel approach to understanding the complex dynamics of the electric vehicle (EV) market through the lens of differential game theory. We developed a comprehensive model that captures the strategic interactions between EV manufacturers and charging network operators, while incorporating the effects of consumer behavior, market uncertainties, and reference price effects. Using differential game theory, we examined the impact of reference price effects and the charging network’s influence on pricing strategies, focusing on three distinct approaches: basic pricing, static pricing considering reference price effects, and dynamic pricing strategies. Our model offers new insights into consumer behavior and price expectations in the rapidly evolving EV market. The key findings reveal that under static or dynamic pricing strategies, the optimal pricing for EV manufacturers is positively correlated with the initial reference price. When the initial reference price is high (low), the optimal pricing strategy resembles skimming pricing (penetration pricing). As the effort level of charging network operators increases and their influence on consumers’ purchase decisions grows stronger, EV manufacturers tend to set higher prices. Notably, while dynamic pricing strategies can optimize EV manufacturers’ profits, the profits of charging network operators may decrease compared with static pricing strategies. This integrated approach significantly contributes to the field by bridging gaps among market dynamics, pricing strategies, and the infrastructure’s development in the context of electric mobility, providing a comprehensive framework for understanding and optimizing the EV ecosystem. Ultimately, this study advances sustainable business models that balance profitability, consumer behavior, and the infrastructure’s growth in the rapidly evolving EV market.

Pricing model of ride-hailing platform considering rationally inattentive passengers

ABSTRACT The ride-hailing services are booming in our daily lives, but it is unclear that how the platforms should set prices to maximize their profits when facing one kind of rationally inattentive passengers in a two-sided market. To fill this gap, we establish a profit maximization model for the ride-hailing platform based on queuing theory and rational inattention theory and analyze the properties of the model. Numerical examples are presented to demonstrate the impacts of perceived high and low service levels, information cost and prior belief on the optimal price and commission rate of the ride-hailing platform. The results show that (1) for different cities, there is always an optimal pricing strategy to maximize the profit of the platform. (2) To ensure maximum profit, the platform should disclose the service information of ride-hailing as much as possible, but also maintain the unknownness of ride-hailing services appropriately.

Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.

To what extent can prisoners dilemma in game theory be used in pricing strategy?

The title of this dissertation is to what extent can prisoners dilemma in game theory be used in pricing strategy? The purpose of the current study was to determine the extent of prisoner's dilemma used in pricing strategies, and how the prisoner's dilemma is used in pricing strategies. We first comprehensive analysis and evaluation of relevant literature about prisoners dilemma and common pricing strategies in literature review section. Then in discussion part, we use game theory models to describe and analyze the prisoner's dilemma in pricing strategies to predict various situations, make the optimal pricing strategies. In the dissertation, we found the effects of prisoners dilemma strategies on payoffs when pricing and explained why cooperative pricing agreements promote mutual benefits and long-term stability than non-cooperative pricing strategies. Then we analyzed the stability of cooperative pricing strategy, we also developed a pricing model based on Bayes theorem. Our research has important implications for economics, marketing and game theory. By applying prisoner's dilemma theory, firms can better understand the behavioral of competitors and enhance decision-making processes, optimize their pricing strategies, thereby enhancing market efficiency and profitability.

Pricing policies for a competitive dual-channel supply chain with green investment and sales effort under revenue-sharing contract

In view of the mushroom development of web-based business and green production, a few organizations have moved to produce ecological friendly items and utilized the online channel to build up the competition. This paper deals with a two-echelon sustainable dual-channel supply chain containing two manufacturers and a single retailer. One manufacturer of the investigated supply chain produces a traditional non-green item, and another one releases a substitutable green item. The demand of both the item at the online and offline channel is a function of the sales price, greenness level, sales effort of the retailer. Furthermore, we have discussed the criteria for optimal pricing and greening strategy of supply chain members and the sales effort level of the retailer under a centralized and decentralized model. To enhance the supply chain performance, a revenue-sharing contract has been proposed, subject to the pareto improvement of each channel member. The numerical result demonstrates that the proposed revenue-sharing contract can enhance the manufacturer’s profit as well as system profit compared to the centralized and decentralized model and ensure higher profit for all the members than the decentralized model. Moreover, the item in the centralized model enjoys the highest green level than all other scenarios. Sensitivity analysis exhibits that a higher value of greening cost reduces the manufacturer’s green level, decreasing the sales price.

Low‐carbon optimal scheduling strategy for multi‐agent integrated energy system of the park based on Stackelberg–Nash game

AbstractUnder the background of achieving the dual‐carbon goal, the park is a natural experimental field for practicing the dual‐carbon goal. A low‐carbon optimal scheduling strategy for a multi‐agent park‐integrated energy system (P‐IES) based on the Stackelberg–Nash game is proposed. Firstly, a low‐carbon P‐IES scheduling model is established, considering the two‐stage operation of power‐to‐gas (P2G) technology and reward–punishment stepwise carbon trading mechanism. Then, a two‐layer game optimisation model is proposed with a park‐integrated energy system operator (P‐IESO) as the leader and multiple low‐carbon P‐IESs as followers. Among them, the parks are connected with each other through the power trading channels. Then, the Nash equilibrium solution is obtained by the iterative search method, and the uniqueness of the equilibrium solution of the game is proven, so as to determine the optimal pricing strategy of the operator. Finally, a typical industrial park is taken as an example for simulation verification. The example analysis verifies that the model can effectively reduce the system's carbon emissions and improve the park's economic benefits and low‐carbon levels, thereby achieving the coordinated development of low‐carbon and economic performance in the park.

Pricing electricity from blue hydrogen to mitigate the energy rebound effect: A case study in agriculture and livestock

As the world's second largest producer of natural gas, Iran relies heavily on natural gas for 70% of its energy needs. There are, however, challenges associated with energy supply in remote regions attached to the gas pipeline network, particularly during the cold season when fuel oil is required for heating and energy provisioning. To address this issue, this research proposes the use of alternative energy sources, such as hydrogen, for non-residential energy consumption, including agriculture and livestock. A key objective of this study is to develop an optimization model for the generation of electricity from hydrogen resources, as well as formulate a robust strategy to assist electricity producers in maximizing their profits. Using this model, producers are able to set prices in accordance with their risk tolerance and present them to the market, thus mitigating price fluctuations. In the targeted sectors, the results indicate that appropriate pricing models can prevent rebound effects. Specifically, the implementation of this model can reduce natural gas consumption by up to 20% and fuel oil usage by 15% during peak seasons. Additionally, the optimal pricing strategy can lead to a 10% reduction in greenhouse gas emissions. The results of this research provide stable and reliable solutions to the energy supply challenges in these regions, thus making the energy market more resilient and environmentally friendly.

Gated ensemble of spatio-temporal mixture of experts for multi-task learning in ride-hailing system

Ride-hailing system requires efficient management of dynamic demand and supply to ensure optimal service delivery, pricing strategies, and operational efficiency. Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner to forecast demand and supply-demand gap in a ride-hailing system poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting these spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.

Asymptotically Tight Bounds on the Optimal Pricing Strategy with Patient Customers

In “Asymptotically Tight Bounds on the Optimal Pricing Strategy with Patient Customers,” Qian, Su, and Zhou revisit the problem of a monopolist seller’s optimal pricing strategy when dealing with both patient and impatient customers. The authors give asymptotically tight bounds on the optimal strategy’s cycle period and long-run average revenue when the patient customers’ patience level is high. This result also addresses an open question proposed by others regarding the optimal cycle period.

Optimal strategies in electric vehicle battery closed-loop supply chain considering government subsidies and echelon utilization

This study examines an electric vehicle battery closed-loop supply chain including a battery manufacturer and a retailer, with a focus on echelon utilization and remanufacturing of waste electric vehicle battery. Optimal pricing strategies and technological effort decision of the supply chain members are derived based on a Stackelberg game model. Three government subsidy policies are considered to promote recycling and reuse of waste electric vehicle batteries, namely, no subsidies, production research and development (R&D) subsidies and recycling subsidies, respectively. Results show that both echelon utilization and recycling subsidies can notably contribute to recycling rate of waste electric vehicle batteries. While echelon utilization boosts the profit of retailer and the whole supply chain, it may have a negative impact on manufacturer's inclination to remanufacturing and its profit. Subsidies for production research and development are preferred by the supply chain and its members from the profit-oriented perspective. Further analyses are conducted under hybrid strategies: hybrid government subsidy policy and hybrid recycling mode. Results show that hybrid strategies could significantly increase the demand and recycling volume, thus positively affecting the profitability of the entire closed-loop supply chain. Our study comprehensively considers the game relationships among manufacture and retailer, enhancing the efficiency of recycling and profitability while also providing a theoretical basis for the government to formulate reasonable subsidy policies.

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.

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.

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.

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.

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.

Optimal Pricing and Retailing Strategy for an Assembled Product Manufacturing–Remanufacturing Process under Carbon Emission Regulations and Autonomation

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by 18.12%, whereas autonomated inspection increased retailer profit by 10.27%.

Mobile telecommunication companies’ investment and pricing strategies for content service

Several mobile telecommunication companies (MTCs), such as AT&T, Verizon, and China Mobile, are investing in content services to drive continued growth as the telecom industry has become saturated. In this study, we investigate the competition between two MTCs, one of which (MTC 1) provides both data and content services, while the other (MTC 2) provides only data services. We propose a two-stage game-theoretic model to analyze the two MTCs' optimal pricing strategies for data service and content service as well as the investment strategy for the content service. Our analysis reveals that a higher value of value-added service offered by MTC 1 will incentivize it to raise its subscription fee for the content service and induce both MTCs to reduce the prices of their data services, which results in an increased demand for the content service and a decreased demand for MTC 2's data service. As MTC 1 invests further in the quality of the content service, it would benefit from increasing the subscription fee for the content service, and the two MTCs should increase the subscription fee for the data service. However, compared to the decisions in the scenario where MTC 1 does not provide content service, the provision of this service by MTC 1 leads to a lower subscription fee for the two MTCs' data services, an increase in the demand for MTC 1's data service, and a decrease in the demand for MTC 2's data service. These findings reveal that regularly monitoring market competition, as well as adjusting pricing strategies based on market scenarios and the value of services, can enhance competitiveness and long-term profitability for MTCs.