Dynamic Pricing for Airline Ancillaries with Customer Context

The article deals with dynamic pricing (DP) models methodological aspects, how to model DP in different spheres of services/industries, which factors have a significant effect on the DP formation and what are the benefits of DP application in practice. This paper discusses the concept of dynamic pricing, the dynamic pricing models principles, problems, main methodological assumptions and methods in retail. Therefore the aim of this paper is to analyse the dynamic pricing models principles and problems and to represent the results of DP forming factors evaluated by AHP method.

Many of the companies started moving towards cloud computing because of its characteristics like pay as you go, easy to use, scalable, multi-tenant, secure, etc. There are many cloud providers providing cloud services over the internet. There are number of pricing policies for the user. These pricing policies can be categorised as static and dynamic pricing. This paper represents the enhancement and evaluation of the proposed dynamic resource pricing model for cloud resources based on the consumer behaviour. For model evaluation, one tail two sample t-test has been used. The results show the model will be helpful to the cloud provider for profit maximisation.

This paper studies the reference effect on dynamic pricing in revenue management for cases with limited capacity and stochastic demand. We first present a single‐period fixed pricing (FP) model in finite horizon with fixed capacity and stochastic demand, and show that there is a unique optimal solution. The model is then extended to a discrete‐time dynamic pricing (DP) model as a benchmark case. We subsequently propose a DP model with reference effect (DPR), investigate the properties of the revenue function, and present a computational scheme to compute the dynamic optimal price. Numerical experiments are conducted to exhibit how the reference effect may influence the initial price, pricing trend, price dispersion, and expected revenue, with the FP, DP, and DPR policies in the same environment of a fixed capacity facing stochastic demand. We also present, through numerical examples, a comparison between deterministic demand and stochastic demand scenarios under reference effect, and with a fixed initial price versus a variable initial price.

A rational pricing model for demand response programs in contemporary distribution systems

This conceptual paper analyzes the use of dynamic pricing in soccer competitions in the United States, emphasizing the role of business analytics in enabling data-driven ticket pricing methods. Based on economic, marketing, and sports management perspectives, we identify a gap in the literature about dynamic pricing in soccer events, which have traditionally depended on static pricing models. Our research examines how dynamic ticket pricing might enhance income and attendance for soccer competitions while ensuring fairness for fans. Our research reveals that dynamic pricing enables organizers to maximize revenue from high-demand matches by increasing ticket prices to align with real-time market value and to enhance attendance for low-demand matches by strategically reducing prices and providing discounts. These modifications are facilitated by business analytics, encompassing demand forecasts and price optimization algorithms, which guarantee that current data informs pricing decisions on consumer willingness to pay. Our analysis emphasizes that dynamic pricing in sports functions not merely as a mechanism for price augmentation but may also augment consumer value and accessibility, as demonstrated by its capacity to lower ticket prices when warranted. Drawing on existing literature, the paper provides a theoretical framework and practical recommendations, highlighting areas for future empirical research. We offer practical insights into the implementation of dynamic pricing in soccer, emphasizing the significance of analytical models, effective stakeholder engagement, and policy considerations to balance revenue objectives with fan experience. These findings enhance sports management literature by applying dynamic pricing theory to U.S. soccer and providing a framework for utilizing business analytics in the optimization of sports revenue.

Due to development of the batteries and it's charging technologies, public incentives and growing criticism on dense air pollution, the electrical vehicles (EVs) gaining more popularity as compared to traditional fuel vehicles. In this paper, the multi-objective optimal scheduling of Electric Vehicle batteries in Battery Swapping Station (BSS) is aimed in order to optimize the number of EV batteries taken from battery stock. Further, degradation cost of batteries and electricity charging cost of batteries also considered for optimal scheduling of EV batteries in BSS. The dynamic electricity pricing model is considered to avoid new peaks of battery charging demand in BSS. EV battery swapping demand in each hour of the day is solved by using a multi-objective Shuffled Frog Leaping Algorithm (SFLA). The simulation results present the effectiveness of multi-objective optimization and dynamic pricing model.

We introduce a new model of user-based dynamic pricing in which decisions occur in real time and are strongly influenced by the budget constraints of users. This model captures the fundamental operation of many electronic markets that are used for allocating resources. In particular, we focus on those used in data centers and cloud computing where pricing is often an internal mechanism used to efficiently allocate virtual machines. We study the allocative properties and dynamic stability of this pricing model under a standard framework of cloud computing systems which leads to highly degenerate systems of prices. We show that as the size of the system grows the user-based budget-constrained dynamic pricing mechanism converges to the standard Walrasian prices. However, for finite systems, the prices can be non-degenerate and the allocations unfair, with large groups of users receiving allocations significantly below their fair share. In addition, we show that improper choice of price update parameters can lead to significant instabilities in prices, which could be problematic in real cloud computing systems, by inducing system instabilities and allowing manipulations by users. We construct scaling rules for parameters that reduce these instabilities.KeywordsCloud ComputingVirtual MachineEquilibrium PriceDynamic PricePrice MechanismThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

We advance the literature on dynamic oligopoly pricing models in the presence of switching costs by additionally modeling the strategic pricing role of the retailer within the distribution channel. In doing this, we study the relative dynamic pricing implications of how current retail and wholesale prices for a brand must optimally take into account past and future demand, respectively, for the brand. Using scanner data from the cola market, we find that while the retailer exploits the benefit of inertial demand by appropriately increasing the retail profit margin, the cost of investing is borne entirely by the manufacturers. We use simulation studies to show how the retailer will lose its ability to leverage the benefits of inertial demand as consumers become more price sensitive. We also show that when inertia of the more price-sensitive customer segment increases, the aggregate welfare of consumers, the retailer, and manufacturers may increase. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2649 . This paper was accepted by J. Miguel Villas-Boas, marketing.

Using the differences in user attribution and profitability among different business models employed by technology‐trading platforms, a two‐stage dynamic game pricing model was constructed considering three pricing strategies. The research found that (1) in the information‐intermediary business model, technology‐trading platforms should adopt a two‐stage pricing model that first emphasizes transaction fees followed by mixed system charges. (2) In the one‐stop service business model, technology‐trading platforms can achieve higher returns by comprehensively using big data analysis to tailor mixed fees. (3) The traditional information‐intermediary platform can adopt a pricing model with mixed system charges to transition to a one‐stop service platform.

In the competitive landscape of modern manufacturing, achieving optimal profit margins necessitates a balanced integration of cost management, strategic pricing, and production efficiency. Manufacturing firms operate in a dynamic environment where fluctuating raw material prices, labour costs, and technological advancements directly impact profitability. To remain sustainable and competitive, companies must adopt systematic approaches that enhance cost efficiency, optimize pricing models, and improve overall production processes. This research paper explores various methodologies and frameworks that manufacturers can employ to maximize profitability while maintaining operational effectiveness. Cost management techniques such as activity-based costing, target costing, and lean manufacturing principles are examined to highlight their role in reducing waste and improving efficiency. By integrating these methodologies, manufacturers can better allocate resources, streamline production, and achieve economies of scale. Moreover, leveraging automation, IoT-enabled predictive maintenance, and data analytics can further enhance cost control and operational agility. Pricing strategy plays a crucial role in profit margin optimization. While traditional cost-plus pricing ensures a predictable margin, dynamic pricing models and value-based pricing provide manufacturers with flexibility to adjust prices based on demand, competition, and customer perception. This paper delves into data-driven pricing mechanisms that enable businesses to maximize revenue while maintaining market competitiveness. Furthermore, penetration and skimming pricing strategies are discussed to illustrate their applicability across different market scenarios. Production efficiency is another key factor influencing profitability. The adoption of lean manufacturing, Six Sigma methodologies, and total quality management (TQM) has proven to enhance operational performance while minimizing defects and production downtime. This study provides an in-depth analysis of how firms can optimize supply chain logistics, enhance workforce productivity, and reduce bottlenecks in manufacturing processes. By implementing smart manufacturing techniques, companies can improve throughput, enhance product quality, and achieve just-in-time production goals. This study also examines real-world case studies and empirical data to illustrate successful applications of these strategies. For instance, the integration of AI-driven analytics in a leading automotive manufacturing company resulted in a 20% reduction in operational costs and a 15% improvement in productivity. Similarly, a consumer electronics firm implemented dynamic pricing algorithms to adapt to market fluctuations, leading to a 10% increase in revenue without additional marketing expenditure. Furthermore, emerging trends such as AI-driven analytics, smart manufacturing, and supply chain digitalization are examined to highlight their potential impact on future profit optimization. The role of Industry 4.0, blockchain technology in supply chain transparency, and automation in reducing human intervention are also discussed. These innovations are transforming the traditional manufacturing landscape and setting new benchmarks for efficiency and profitability.

The platform economy evolved rapidly due to technological breakthroughs, hence new challenges emerged in terms of pricing strategies affected from different user behaviors and market demand dynamics. Traditional pricing methods are not up to this type of challenge, and it requires a model able to be more agile — in real time. The study presents “DeepPrice,” a dynamic pricing model using deep learning namely Convolutional Neural Networks (CNN) and Deep Reinforcement Learning (DRL) to achieve the optimal platform pricing strategies in response to user behavior and market signals. The research has an experimental design for the development and testing of the DeepPrice model. The model is pre-trained by the transaction data of an important e-commerce platform for this task. CNNs learn user profiles and product properties via the encoding layer, while DRL models implement the strategy of adjusting price according to behavior actions in tensor form. Metrics such as platform revenue, user conversion rates, and customer satisfaction are used to validate the model back to the model performance. Our solution helped DeepPrice to generate incremental 20% platform revenue on average and better adjust to the market challenges. It performed better than standard pricing solutions, especially in times of high demand, and effectively personalized price-pointing for top-value customers to drive higher conversions and improve customer satisfaction. This study points to the promise of using deep learning to improve dynamic pricing in platform economies. Flexible & Scalable Solution for any IndustryDeepPrice Nevertheless, challenges regarding the computational cost of implementing personalized pricing strategies and ethical debates surrounding such strategies remain to be studied in more detail. In platform economy, the reinforcement learner has a significant potential to provide a reliable real-time pricing solution with CNN in DeepPrice to improve both profit and customer satidfaction.

In this paper, we take the advantage of high frequency data to develop option pricing model and select the Realized GARCH model to describe the volatility of assets, use NIG distribution to describe the distribution of underlying assets, and also build the Realized-GARCH-NIG model to price the option. Finally, we obtain the dynamic option pricing model based on the Realized-GARCH-NIG approach. To verify the effect of the dynamic option pricing model based on the Realized-GARCH- NIG approach, this paper provides the empirical analysis between the dynamic option pricing model based on the Realized-GARCH-NIG approach and the B-S option pricing model. The results show that the option value obtained from the dynamic option pricing model based on the Realized-GARCH-NIG approach is more accurate and effective than the B-S option pricing model.

In this article, we explore the current‐state of literature on how dynamic pricing models incorporate consumer reference‐price effects in developing more informed dynamic pricing strategies for products that have repeated consumer interactions. We first examine the literature on how consumer demand is impacted by reference‐price effects and how consumers form reference prices through their purchase experience. We then explore, in detail, the dynamic pricing models with consumer reference‐price effects in major studies, in order to highlight their key findings and insights. We conclude our review by pointing out research gaps and future research directions on dynamic pricing area in the presence of consumer reference‐price effects.

In this paper we propose two models for dynamic pricing of next generation networks services. The first model includes algorithms based on Stackelberg game between a service provider and users. In the second model Nash game is applied for modelling the relationship between two competing service providers.

We study a multi‐period, multi‐item dynamic pricing problem faced by a retailer. The objective is to maximize the total profit by choosing prices, while satisfying several business rules. The strength of our work lies in our graphical model reformulation, which allows us to use ideas from combinatorial optimization. We do not make any assumptions on the structure of the demand function. The complexity of our method depends linearly on the number of time periods but is exponential in the memory of the model (number of past prices that affect current demand) and in the number of items. We prove that the profit maximization problem is NP‐hard by showing an approximation preserving reduction from the weighted Max‐3‐SAT problem. We next introduce the discrete reference price model which is a discretized version of the reference price model, accounting for an exponentially smoothed contribution of all past prices. We show that our problem can be solved efficiently under this model. We then approximate common demand functions using the discrete reference price model. To handle cross‐item effects among multiple items, we propose to use a virtual reference price that assigns a reference price for each category of items (as opposed to a reference price for each item). To enhance the tractability of our approach, we cluster items into blocks and show how to adapt our method to include business constraints across blocks. Finally, we apply our solution approach using demand models calibrated with supermarket data and validate its practical performance.