Optimal Pricing Research Articles

Designing Loot Boxes: Implications for Profits and Welfare

We study the optimal prices and allocation probabilities for loot boxes and their implications for profits and social welfare.

Enhancing Make-to-Order Manufacturing Agility: When Flexible Capacity Meets Dynamic Pricing

The rise of online marketplaces has raised customer expectations regarding customization and lead time. It poses significant challenges to manufacturing firms and prompts a move from make-to-stock to a more flexible make-to-order system. Compared to make-to-stock settings, make-to-order systems cannot smooth fluctuations in demand using available stock. While viewing dynamic pricing as a useful strategy to balance supply with demand, many manufacturing firms can also create capacity flexibility. In that scenario, system costs could be cut by managing capacity and demand simultaneously. In this paper, we consider a make-to-order production environment with base and surge capacity as well as the ability to adjust product pricing. Our main focus is on operational decision-making, assuming that the base capacity and surge capacity are fixed, but activating the surge capacity incurs a setup cost. Initially, we propose a stochastic control model to reflect this complex decision problem. However, our initial model leads to an intractable dynamic programming problem. To overcome this, we convert the problem to a more tractable diffusion control problem. This approach helps to reveal the conditions under which utilizing flexible capacity is more advantageous than relying solely on fixed capacity. When flexible capacity is advantageous, we provide a solution to the diffusion control problem that can guide optimal capacity and price adjustments. We discover an interesting interplay between capacity adjustment and dynamic pricing. In particular, we find that the price, which aims at reducing congestion, may not monotonically increase with the congestion level when capacity adjustments incur a fixed cost.

An Effective Method of Equivalent Load-Based Time of Use Electricity Pricing to Promote Renewable Energy Consumption

The variability and intermittency inherent in renewable energy sources poses significant challenges to balancing power supply and demand, often leading to wind and solar energy curtailment. To address these challenges, this paper focuses on enhancing Time of Use (TOU) electricity pricing strategies. We propose a novel method based on equivalent load, which leverages typical power grid load and incorporates a responsibility weight for renewable energy consumption. The responsibility weight acts as an equivalent coefficient that accurately reflects renewable energy output, which facilitates the division of time periods and the development of a demand response model. Subsequently, we formulate an optimized TOU electricity pricing model to increase the utilization rate of renewable energy and reduce the peak–valley load difference of the power grid. To solve the TOU pricing optimization model, we employ the Social Network Search (SNS) algorithm, a metaheuristic algorithm simulating users’ social network interactions to gain popularity. By incorporating the users’ mood when expressing opinions, this algorithm efficiently identifies optimal pricing solutions. Our results demonstrate that the equivalent load-based method not only encourages renewable energy consumption but also reduces power generation costs, stabilizes the power grid load, and benefits power generators, suppliers, and consumers without increasing end users’ electricity charges.

Game theory-based optimization for efficient IoT task offloading in 6G network base stations

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization.

The two‐stage presale pricing for online products considering anchoring effect and payment pain reduction

AbstractOnline product pricing is an important research field in the platform economy, and a two‐stage mixed presale is a mainstream mode of online sales. In the process of online consumption, the payment method and presale mode have an important impact on consumer behaviour and product pricing. Based on the two‐stage mixed presale mode, this study considers the impact of anchoring and the payment pain reduction effect on consumer behaviour, establishes the corresponding product pricing model and obtains the optimal presale price, sales and profit. It sheds light on the influence mechanism of anchoring and the payment pain reduction effect on consumer behaviour and online product pricing. Theoretical and numerical analyses reveal that the payment pain reduction effect can help to raise the product price, stimulate market vitality and bring higher profits for online retailers, and the anchoring effect and consumers' loss aversion psychology can enhance the profits and sales of online retailers, but they will discourage setting high prices for products. The research results will further enrich the relevant research on customer behaviour and product pricing in the platform economy and will provide decision bases for platform enterprises and online retailers to participate in presale activities and formulate product pricing strategies.

Low-carbon economic operation strategy for multi-agent integrated energy system considering uncertainty of renewable energy power generation

The uncertainty of renewable energy output threatens the operation safety of multi-agent integrated energy system (MAIES), which makes it difficult to balance the low-carbon economic operation demands of various stakeholders. However, the existing research solely focuses on the operational strategy of multi-agent game involving integrated energy suppliers and users in deterministic scenarios, overlooking the complementary supporting role and game interaction of shared energy storage and wind farm as independent entities of interest under the instability of renewable energy power generation. Hence, this paper first establishes the optimal operation models for integrated energy system operator (IESO), user aggregator (UA), shared energy storage operator (SESO), and wind farm operator (WFO) considering the stepped carbon trading. Second, in the face of the actual situation of uncertainty of photovoltaic and wind power output, fuzzy chance-constrained programming is adopted for processing. Then, a bi-layer game equilibrium model with IESO as a leader and UA, SESO, and WFO as followers is proposed, and the existence and uniqueness of Stackelberg equilibrium solution are proved. Finally, simulation calculation is carried out based on the YALMIP toolbox in the Matlab R2023a software, and the improved particle swarm optimization algorithm and CPLEX solver are used to solve the model. The results demonstrate that the participation of SESO and WFO as independent stakeholders in the game interaction can improve the economic and environmental benefits of MAIES. The iterative optimization of demand response subsidy prices can effectively motivate users to participate in demand response, improve the ability of MAIES to cope with the uncertain risks of renewable energy generation and load, and reduce the power grid dispatch pressure.

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Optimal Location and Pricing of Electric Vehicle Charging Stations Using Machine Learning and Stackelberg Game

Pricing carbon in a multi-sector economy with social discounting

Economists tend to view a uniform emissions price as the most cost-effective approach to reducing greenhouse gas emissions. This paper scrutinizes the assumptions in general equilibrium which underlie the established view that uniform emissions pricing is optimal, focusing on economies where society values the well-being of future generations more than private actors. When social and private discount rates differ, we show that a uniform carbon price is optimal only under restrictive assumptions about technology homogeneity and intertemporal decision-making. Non-uniform pricing spurs capital accumulation and benefits future generations. Depending on sectoral heterogeneity in the substitutability between capital and energy inputs, we find that optimal carbon prices differ widely across sectors and yield substantial welfare gains relative to uniform pricing. Realizing these welfare gains, however, requires that the regulator has information on the technology heterogeneity across sectors. Differentiated carbon pricing based on imperfect estimates of technology heterogeneity can yield significant welfare losses.

Bundling in Advance Sales: Theory and Evidence from Round‐Trip versus Two One‐Way Tickets*

We theoretically derive an optimal price for a bundle of two goods that are sold in advance to risk‐averse buyers. The theory predicts that a round‐trip ticket is less expensive than two one‐way tickets when demands for the outbound and the inbound are uncertain and positively correlated. Using a unique airlines dataset, we find evidence that is consistent with the theory; round‐trip bundle discounts exist and they are larger for passengers who buy early in advance, stay on a Saturday night, and have higher valuations. We also find that the bundle discounts decrease with competition.

Video platforms’ advertising and pricing decisions in the presence of derivative videos

Derivative videos generated based on copyright videos are a common type of user-generated content (UGC) on today’s video platforms. Given the role of derivative videos in resolving consumers’ uncertainty about the quality of paid copyright videos, and their potential to generate advertising revenue, we develop a game-theoretic model to investigate a video platform’s optimal joint-decisions on pricing and advertising to maximize the utilization of derivative videos. We find that including advertisements into derivative videos to derive advertising revenue is not always advantageous for platforms. Under certain conditions, it is more beneficial for platforms to exclude advertisements from derivative videos. The presence of a derivative video may not affect the optimal price of the copyright video. Furthermore, a higher consumer initial quality expectation for the copyright video does not necessarily lead to a greater platform profit in the presence of a derivative video.

Optimal information disclosure in a production-inventory queueing system

This study considers a production-inventory queueing system, where customers decide whether to wait for the item or leave without a purchase if a stockout occurs. The system manager determines the joint pricing and inventory control policy. In addition, the system manager can choose whether or not to provide information about the system state to arriving customers. Our strategic production-inventory queueing system includes the observable, unobservable, and almost unobservable systems, which are obtained by appropriately choosing the information disclosure policy by the system manager. In this study, we find the optimal information disclosure policy, as well as the optimal price and the optimal inventory control policy for the profit maximization and social welfare maximization. Under the optimal policy, the system manager should disclose the system state if and only if the queue length exceeds a certain threshold.

Deep Learning Pricing of Processing Firms in Agricultural Markets

The pricing behavior of agricultural processing firms in input markets has large impacts on farmers’ and processors’ prosperity as well as the overall market structure. Despite analytical approaches to food processors’ pricing in agricultural input markets, the need for models to represent complex market features is urgent. Agent-based models (ABMs) serve as computational laboratories to understand complex markets emerging from autonomously interacting agents. Yet, individual agents within ABMs must be equipped with intelligent learning algorithms. In this paper, we propose supervised and unsupervised learning agents to simulate the pricing behavior of firms in agricultural markets’ ABMs. Supervised learning firms are pre-trained to accurately best respond to their competitors and are deemed to result in the market Nash equilibria. Unsupervised learning firms play a course of pricing interaction with their competitors without any pre-knowledge but based on deep reinforcement learning. The simulation results show that unsupervised deep learning firms are capable of approximating the pricing equilibria obtained by the supervised firms in different spatial market settings. Optimal discriminatory and uniform delivery pricing emerges in agricultural input markets with the high and intermediary importance placed on space. Free on board pricing emerges in agricultural input markets with small importance placed on space.

Impact of Marketing Strategies of Retailers on Consumers with Specific Reference to Ramki Bandi and Local Tiffin Centres

Entrepreneurs and small business management go hand in hand in making a successful business. The study is taken up on retailers in the street food industry. The study’s objective is to analyze the impact of the marketing strategies of Ram ki Bandi , a famous street food corner in Hyderabad, India, and a comparative study between the marketing strategies of Ram ki Bandi and local street tiffin centers. Tiffin centers are small eateries put up on the street. A marketing strategy is a long-term plan for achieving a company’s goals by understanding customers’ needs and creating a distinct and sustainable competitive advantage. For the study, the data was collected through primary data sources, such as survey methods, questionnaires, and secondary data sources. The primary data was collected from a sample of 100 respondents using questionnaires and interviews. Marketing strategies are essential in the growth of a retailer’s business. Some of them are as follows: attracting more customers, analyzing competitors, understanding customers, and making optimal pricing decisions, amongst others. The findings of the study state that marking strategies impact consumer behavior when purchasing a given product.

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

PurposeThis research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.Design/methodology/approachA feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.FindingsThe findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.Practical implicationsBased on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.Originality/valueThe present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information

How to Manage Horizontally Differentiated Products When Customer Preferences and Demand Distributions Are Unknown In the paper “Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information,” we consider the problem of determining the optimal prices and product configurations of horizontally differentiated products when customers purchase according to a locational choice model and where the problem parameters are initially unknown to the decision maker. We propose a data-driven algorithm that learns the optimal prices and product configurations from accumulating sales data, and we show that their regret—the expected cumulative loss caused by not using optimal decisions—after T time periods is [Formula: see text]. We accompany this result by proving an almost-matching lower bound of regret, implying that our algorithms are asymptotically near optimal. In an extension, we show how our algorithm can be adapted for the case of fixed locations.

An offloading and pricing mechanism based on virtualization in edge–cloud computing

Integrate users, edge servers and cloud into a system can improve resource utilization and quality of service (QoS). The users can send the same request to multiple Edge Servers. However, edge servers have limited scope, which may result in repeated offloading tasks for the same user. In this paper, we consider the cloud resource migration in the form of virtual machines (VMs) to achieve high computing efficiency and low latency requirements. Obviously, the migration of VMs causes changes in the number and price of edge resources, which further affects the task offloading strategy. Therefore, it is necessary to optimize the VMs migration strategy and resource pricing strategy successively to achieve the optimal offloading strategy suitable for users. First, based on the competition between edge servers, a static VMs migration algorithm (SVMMA) is designed to formulate the strategy and price of VMs migration. Then, according to the users’ resource needs and the resource possession of different edge servers, a resource partitioning approach (RPA) have been proposed. Finally, two different QoS functions are evaluated, representing the QoS matching degree between tasks and multiple resource combination blocks (MRCBs). Wherein, a dynamic matching and pricing of tasks algorithm (DMPT) is conducted to obtain the optimal offloading strategy and price of the task. Experiments show that our algorithms can improve the offloading rate of tasks and achieve the relative balance among user, cloud server, and edge server benefits.

Fresh Produce Ordering, Pricing and Freshness-Keeping Decisions with Call Option Contracts and Spot Markets

Considering the characteristics of both quality and quantity losses in fresh produce as well as the existence of spot markets, optimal retailer ordering, pricing, and freshness-keeping decisions through the single ordering policy (firm ordering only or option ordering only) and the mixed ordering policy (firm ordering and option ordering simultaneously) are constructed based on option contracts and analyzed for the retailer under different ordering policies. The results show that there is a unique optimal pricing, ordering, and freshness-keeping decision under all three ordering policies, but there is no joint decision. The optimal freshness-keeping and retail price under the mixed ordering policy are lower than those under the option ordering only but higher than those under the firm ordering only. When only a single order can be placed, the retailer’s optimal ordering policy is determined by demand risk. When all three ordering policies are available, the optimal ordering policy for the retailer is the mixed ordering policy. A spot market will weaken the role of option contracts in mitigating supply chain risks, and the larger the risk, the more significant the role of the spot market.

Research on automatic pricing and replenishment strategy of vegetable commodities based on ARIMA algorithm

In just a few years' time, supermarkets have transformed from a novelty into a mainstream channel that unifies the retail world. Since superstores sell vegetables with a wide variety of types, different origins, and shorter freshness periods, the incoming transaction time of vegetables is often in the early morning, so superstores usually replenish their stocks on a daily basis according to the historical sales and demand of each commodity. First, this paper visualizes the historical data of vegetable commodity purchasing and pricing in superstores in order to observe the distribution characteristics and trends, and then analyzes the correlation between categories or individual products using statistical methods such as correlation factors and covariance. To predict replenishment and pricing strategies for the coming week, this paper analyzes the relationship between overall sales and cost-plus pricing for each vegetable category, using given sales data to establish the relationship between sales volume and price for each category. Based on the ARIMA predicted sales model and given costs, the optimal replenishment quantities and pricing strategies were determined using an optimization algorithm.

Research on the Optimal Pricing for Life Insurance

Term life insurance is a policy that guarantees the buyer of the policy insurance benefit upon their death for a period of time in exchange for a premium paid by the insurance buyer to the seller yearly for a set number of years. Purchasing life insurance has become an important, if not necessary, financial investment in everyones life, as it protects their family from a potentially devastating financial loss if they unexpectedly pass away. This paper combined multiple variables in order to compute a pricing formula for life insurance. The final result is that as the age of purchase increases, the premium, or, in other words, the price of the life insurance using my pricing formula, increases exponentially, while the profit received decreases exponentially.

Deep Hawkes process for high-frequency market making

AbstractHigh-frequency market making is a liquidity-providing trading strategy that simultaneously generates many bids and asks for a security at ultra-low latency while maintaining a relatively neutral position. The strategy makes a profit from the bid-ask spread for every buy and sell transaction, against the risk of adverse selection, uncertain execution and inventory risk. We design realistic simulations of limit order markets and develop a high-frequency market making strategy in which agents process order book information to post the optimal price, order type and execution time. By introducing the Deep Hawkes process to the high-frequency market making strategy, we allow a feedback loop to be created between order arrival and the state of the limit order book, together with self- and cross-excitation effects. Our high-frequency market making strategy accounts for the cancellation of orders that influence order queue position, profitability, bid-ask spread and the value of the order. The experimental results show that our trading agent outperforms the baseline strategy, which uses a probability density estimate of the fundamental price. We investigate the effect of cancellations on market quality and the agent’s profitability. We validate how closely the simulation framework approximates reality by reproducing stylised facts from the empirical analysis of the simulated order book data.