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Journal articles on the topic 'Dynamic Pricing Models'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Lembhe, Pankaj. "Dynamic Toll Pricing Models and Traffic Flow Optimization." International Journal of Science and Research (IJSR) 9, no. 11 (2020): 1716–22. http://dx.doi.org/10.21275/sr24314032137.

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PONOMARENKO, Ihor, and Dmytro PONOMARENKO. "Dynamic pricing in marketing." Scientia fructuosa 161, no. 3 (2025): 74–89. https://doi.org/10.31617/1.2025(161)05.

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Forming effective marketing communications with the target audience in the digital environment involves implementing a modern and flexible pricing system that considers the dynamics of changes in a set of factors. A hypothesis has been formulated that dynamic pricing based on machine learning algorithms allows businesses to achieve optimal demand for goods and services of companies, and also helps to ensure the loyalty of consumers to the brands in the long term. Conducting the research, general scientific methods of analysis and synthesis were used to characterize the main strategies of dynam

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Lehmann, Bruce N. "Notes on dynamic factor pricing models." Review of Quantitative Finance and Accounting 2, no. 1 (1992): 69–87. http://dx.doi.org/10.1007/bf00243985.

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Narahari, Y., C. V. L. Raju, K. Ravikumar, and Sourabh Shah. "Dynamic pricing models for electronic business." Sadhana 30, no. 2-3 (2005): 231–56. http://dx.doi.org/10.1007/bf02706246.

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Ait Bachir, Lynda, Oumelkheir Elbaroud, and Fatma Bouderra. "Dynamic Pricing Models for Automobile Insurance." Financial Markets, Institutions and Risks 9, no. 1 (2025): 162–94. https://doi.org/10.61093/fmir.9(1).162-194.2025.

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The accurate pricing of automobile insurance remains a critical challenge, particularly in markets where traditional models fail to capture risk heterogeneity. This study addresses the limitations of conventional Poisson models, which assume uniform accident probabilities among insured individuals, by incorporating advanced probabilistic models that account for overdispersion and individual risk variability. The primary objective is to develop a more precise and equitable pricing model for automobile insurance premiums, integrating statistical and Bayesian inference techniques. The research fo

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Balogun Segun Segbenu, Mariam Olateju, Adebayo Sulaimon Olawale, and Victoria Kujore. "Applications of Reinforcement Learning in Dynamic Pricing Models for E-Commerce Businesses." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 1562–73. https://doi.org/10.30574/wjarr.2025.26.3.2319.

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Dynamic pricing has become a cornerstone strategy for e-commerce businesses seeking to optimize revenue while maintaining competitive advantage in rapidly changing digital markets. This review examines the integration of reinforcement learning techniques into dynamic pricing models, exploring how these adaptive algorithms enable businesses to make real-time pricing decisions based on market conditions, consumer behavior, and competitive dynamics. The research synthesizes current methodologies, implementation frameworks, and performance outcomes across various e-commerce sectors. Reinforcement

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He, Zhongzhi (Lawrence), Sahn-Wook Huh, and Bong-Soo Lee. "Dynamic Factors and Asset Pricing." Journal of Financial and Quantitative Analysis 45, no. 3 (2010): 707–37. http://dx.doi.org/10.1017/s0022109010000207.

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AbstractThis study develops an econometric model that incorporates features of price dynamics across assets as well as through time. With the dynamic factors extracted via the Kalman filter, we formulate an asset pricing model, termed the dynamic factor pricing model (DFPM). We then conduct asset pricing tests in the in-sample and out-of-sample contexts. Our analyses show that the ex ante factors are a key component in asset pricing and forecasting. By using the ex ante factors, the DFPM improves upon the explanatory and predictive power of other competing models, including unconditional and c

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Divya, Chockalingam. "Dynamic Pricing Strategies in Retail: How Customer Analytics Can Optimize Pricing Models." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 1 (2020): 1–4. https://doi.org/10.5281/zenodo.15054790.

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Dynamic pricing has revolutionized the retail sector, allowing businesses to adjust prices in real-time based on customer behavior, competitor pricing, and market demand. This paper explores how customer analytics can optimize dynamic pricing models, improving revenue, customer satisfaction, and market competitiveness. By leveraging big data and machine learning, retailers can implement intelligent pricing strategies that respond to evolving consumer patterns. The study also highlights case studies of successful dynamic pricing implementations and discusses the challenges associated with these

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Behera, Prashanta kumar, and Dr Ramraj T. Nadar. "Dynamic Approach for Index Option Pricing Using Different Models." Journal of Global Economy 13, no. 2 (2017): 105–20. http://dx.doi.org/10.1956/jge.v13i2.460.

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Option pricing is one of the exigent and elementary problems of computational finance. Our aims to determine the nifty index option price through different valuation technique.Â In this paper, we illustrate the techniques for pricing of options and extracting information from option prices. We also describe various ways in which this information has been used in a number of applications. When dealing with options, we inevitably encounter the Black-Scholes-Merton option pricing formula, which has revolutionized the way in which options are priced in modern time. Black and Scholes (1973) and Mer

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Researcher. "DYNAMIC SAAS PRICING: IMPLEMENTING USAGE-BASED MODELS FOR ENHANCED CUSTOMER VALUE." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1650–62. https://doi.org/10.5281/zenodo.14243940.

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This article explores the transformative evolution of Software-as-a-Service (SaaS) pricing models, focusing on the shift from traditional subscription-based approaches to dynamic, consumption-based pricing strategies. The article examines how this transition has fundamentally changed the way software services are valued and delivered to customers, improving both provider sustainability and customer satisfaction. Through comprehensive article analysis of implementation strategies, challenges, and best practices, the article demonstrates how modern technological capabilities enable real-time usa

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Tolulope O Jagun, Olu James Mbanugo, and Olusegun Jimoh. "Integrating dynamic pricing models with pharmacy benefit manager strategies to enhance medication affordability and patient adherence." World Journal of Advanced Research and Reviews 25, no. 3 (2025): 171–87. https://doi.org/10.30574/wjarr.2025.25.3.0696.

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Rising prescription drug costs present a significant barrier to medication adherence and healthcare affordability, requiring innovative pricing strategies to balance cost control, patient access, and pharmacy benefit manager (PBM) collaboration. Traditional fixed pricing models often fail to adapt to patient-specific financial constraints and fluctuating market dynamics, leading to increased non-adherence rates and financial inefficiencies. The integration of dynamic pricing models with PBM strategies offers a scalable solution to enhance medication affordability while ensuring sustainable rei

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Bhageerath Bogi. "The Role of Dynamic Pricing Models in Increasing Marketplace Profitability." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 958–72. https://doi.org/10.32628/cseit2410612401.

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Dynamic pricing models are extremely relevant in today's marketplaces where companies can switch their profitability based on real-time data as it shifts in the markets. The paper is an attempt to explain the theoretical frames of dynamic pricing as well as its technological and real applications in various marketplaces. A real-life investigation into how ML and AI create price optimization mechanisms, thereby impacting the profitability levels in marketplaces. Demand forecasting, market segmentation, and elasticity are the major drivers measured. The paper also took into consideration the eth

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Bhageerath Bogi. "The Role of Dynamic Pricing Models in Increasing Marketplace Profitability." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 988–1002. https://doi.org/10.32628/cseit2410612410.

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Dynamic pricing models are extremely relevant in today's marketplaces where companies can switch their profitability based on real-time data as it shifts in the markets. The paper is an attempt to explain the theoretical frames of dynamic pricing as well as its technological and real applications in various marketplaces. A real-life investigation into how ML and AI create price optimization mechanisms, thereby impacting the profitability levels in marketplaces. Demand forecasting, market segmentation, and elasticity are the major drivers measured. The paper also took into consideration the eth

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Dolgui, Alexandre, and Jean-Marie Proth. "Stochastic Dynamic Pricing Models of Monopoly Systems." IFAC Proceedings Volumes 42, no. 4 (2009): 1469–80. http://dx.doi.org/10.3182/20090603-3-ru-2001.0585.

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van den Goorbergh, Rob W. J., Christian Genest, and Bas J. M. Werker. "Bivariate option pricing using dynamic copula models." Insurance: Mathematics and Economics 37, no. 1 (2005): 101–14. http://dx.doi.org/10.1016/j.insmatheco.2005.01.008.

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Deksnytė, Indrė, and Zigmas Lydeka. "Dynamic pricing models and its methodological aspects." Applied Economics: Systematic Research 7.2, no. 7.2 (2013): 143–53. http://dx.doi.org/10.7220/aesr.1822.7996.2013.7.2.10.

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Das, Pritom, Tamanna Pervin, Biswanath Bhattacharjee, et al. "OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS." American Journal of Engineering and Technology 06, no. 12 (2024): 163–77. https://doi.org/10.37547/tajet/volume06issue12-15.

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This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other m

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Stavinova, Elizaveta, Ilyas Varshavskiy, Petr Chunaev, Ivan Derevitskii, and Alexander Boukhanovsky. "Dynamic Pricing for the Open Online Ticket System: A Surrogate Modeling Approach." Smart Cities 6, no. 3 (2023): 1303–24. http://dx.doi.org/10.3390/smartcities6030063.

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Dynamic pricing is frequently used in online marketplaces, ticket sales, and booking systems. The commercial principles of dynamic pricing systems are often kept secret; however, their application causes complex changes in human behavior. Thus, a scientific tool is needed to evaluate and predict the impact of dynamic pricing strategies. Publications in the field lack a common quality evaluation methodology, public data, and source code, making them difficult to reproduce. In this paper, a data-driven method, DPRank, for evaluating dynamic pricing systems is proposed. DPRank first builds a surr

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Cornacchione, Edgard Bruno, Luciane Reginato, Joshua Onome Imoniana, and Marcelo Souza. "Dynamic Pricing Models and Negotiating Agents: Developments in Management Accounting." Administrative Sciences 13, no. 2 (2023): 57. http://dx.doi.org/10.3390/admsci13020057.

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Linking decision systems, negotiating agents, management accounting, and computational accounting, this paper aims at exploring dynamic pricing strategies of a synthetic business-to-consumer online operation and a comparative analysis of evolving strategy-specific pricing optimization. Five price models based on market, utility, or demand information (three single and two combined), merging online and offline data, are explored over a seven-day period and with twenty selected products. A total of 17,529 website visits and 538 agent negotiations are studied (94,607 main data points) using a Pyt

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GERSHUN, NATALIA, and SHARON G. HARRISON. "ASSET PRICING IN DYNAMIC STOCHASTIC GENERAL EQUILIBRIUM MODELS WITH INDETERMINACY." Macroeconomic Dynamics 12, no. 1 (2007): 50–71. http://dx.doi.org/10.1017/s1365100507060373.

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We explore asset pricing in the context of the one-sector Benhabib-Farmer-Guo (BFG) model with increasing returns to scale in production and compare our results with financial implications of the standard dynamic stochastic general equilibrium (DSGE) model. Our main goal is to determine the effects of local indeterminacy and the presence of sunspot shocks on asset pricing. We find that the BFG model does not adequately represent key stylized facts of U.S. capital markets and does not improve on the asset-pricing results obtained in the standard DSGE model.

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Tan, Yee-Fan, Lee-Yeng Ong, Meng-Chew Leow, and Yee-Xian Goh. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising." Future Internet 13, no. 10 (2021): 241. http://dx.doi.org/10.3390/fi13100241.

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the tim

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Dr.A.Shaji, George. "Realizing the Promise of Dynamic Pricing Through Responsible Innovation." Partners Universal International Research Journal (PUIRJ) 03, no. 03 (2024): 21–37. https://doi.org/10.5281/zenodo.13822630.

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Dynamic pricing, or the practice of altering rates in response to demand, is becoming more common in businesses such as air travel, hotels, and entertainment. While the economic idea of balancing supply and demand through pricing is sound, dynamic pricing implementations have sparked customer skepticism. The lack of transparency in demand metrics and price setting fuels the perception that the systems are manipulative ploys to overcharge clients. Though dynamic pricing tries to maximize revenue when demand spikes, customers believe the slant is exclusively upward during peak hours. Case studie

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Tilak, Bhujade, Hingankar Harshal, Charde Janvi, and Tejal Irkhede Dr. "Dynamic Airline Pricing System." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 2900–2907. https://doi.org/10.5281/zenodo.14944858.

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Competition over fare control has reached a new level of complexity in the airline industry through machine learning to determine the most effective ticket pricing strategies. This research paper demonstrates an ideal dynamic pricing model developed on the programming language Python including preprocessing of data, selection of features and other state of the art models Random Forest and Prophet model among others. The model takes data flights details, economic conditions, weather conditions, and customers demographics of the flight to predict ticket prices correctly. Due to the interface, im

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Wie, Byung-Wook, and Roger L. Tobin. "Dynamic congestion pricing models for general traffic networks." Transportation Research Part B: Methodological 32, no. 5 (1998): 313–27. http://dx.doi.org/10.1016/s0191-2615(97)00043-x.

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Adrian, Tobias, Richard K. Crump, and Emanuel Moench. "Regression-based estimation of dynamic asset pricing models." Journal of Financial Economics 118, no. 2 (2015): 211–44. http://dx.doi.org/10.1016/j.jfineco.2015.07.004.

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Bernstein, Fernando, and Awi Federgruen. "Dynamic inventory and pricing models for competing retailers." Naval Research Logistics 51, no. 2 (2004): 258–74. http://dx.doi.org/10.1002/nav.10113.

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Nalamothu, Pooja Pranavi. "Comparative Analysis of Regression Models for Price Prediction of Ride-On-Demand Services." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1687–700. http://dx.doi.org/10.22214/ijraset.2023.51770.

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Abstract: In recent years, Ride-on-Demand (RoD) services such as Uber, Ola, and Rapido have emerged as popular alternatives to traditional taxi/cab services. These services operate 24/7 and cater to tens of thousands of customers. Unlike traditional cabs, RoD services do not offer a fixed price. Instead, they utilize Dynamic Pricing to balance supply and demand, taking into account factors such as location, time of booking, ride demand, and driver availability to improve their service. However, the unpredictable and fluctuating nature of dynamic pricing has posed a significant challenge for cu

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Torkunova, J. V., V. Y. Ilichev, V. Drach, F. L. Chubarov, and A. N. Paсukevich. "UTILIZING DEEP LEARNING TECHNOLOGIES TO FORM PRICING MODELS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 6/3, no. 147 (2024): 262–69. http://dx.doi.org/10.36871/ek.up.p.r.2024.06.03.032.

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The work is devoted to evaluating pricing models for calculating the profitability of individual financial instruments, for example, such as stocks, using a multilayer generative-adversarial artificial neural network (GAN) and developing its own model based on the analysis. A huge amount of specially selected data is supplied to the inputs of the neural network, changing over time (dynamic). To improve the objectivity of the model, this work does not implement the arbitration capabilities of the markets. This is how one can analyze and explain variations and errors in pricing, as well as ident

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Zhang, Qi, Qiang Shi, Bilal Alatas, and Yu-His Yuan. "Optimization of Dynamic Pricing Models for Consumer Segmentation Markets and Analysis of Big Data-Driven Marketing Strategies." Journal of Organizational and End User Computing 37, no. 1 (2025): 1–33. https://doi.org/10.4018/joeuc.368840.

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In response to the challenges posed by globalization and rapid technological advancements, traditional static pricing models are no longer sufficient to capture the dynamic nature of consumer behavior and market fluctuations. This study proposes a “Multi-dimensional Dynamic Pricing Optimization and Consumer Behavior Prediction Model Driven by Big Data,” which integrates multi-source data and reinforcement learning to improve dynamic pricing strategies. Through a hybrid model architecture using Random Forest and LSTM, it captures both static and time-series features. Experimental results show t

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Lahouel, Noureddine, and Slaheddine Hellara. "Improving the option pricing performance of GARCH models in inefficient market." Investment Management and Financial Innovations 17, no. 2 (2020): 14–25. http://dx.doi.org/10.21511/imfi.17(2).2020.02.

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Understanding the relation between option pricing and market efficiency is important. Indeed, emphasizing this relation generates new insights that are appropriate in practice. These insights give a better understanding of the current limitations of the option pricing and hedging methods. This article thus aims to improve the performance of the option pricing approach. To start, the relation between the option pricing methodology and the informational market efficiency was discussed. It is, therefore, useful, before proceeding to apply the standard risk-neutral approach, to check the efficienc

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Christensen, Timothy M. "NONPARAMETRIC IDENTIFICATION OF POSITIVE EIGENFUNCTIONS." Econometric Theory 31, no. 6 (2014): 1310–30. http://dx.doi.org/10.1017/s0266466614000668.

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Important features of certain economic models may be revealed by studying positive eigenfunctions of appropriately chosen linear operators. Examples include long-run risk–return relationships in dynamic asset pricing models and components of marginal utility in external habit formation models. This paper provides identification conditions for positive eigenfunctions in nonparametric models. Identification is achieved if the operator satisfies two mild positivity conditions and a power compactness condition. Both existence and identification are achieved under a further nondegeneracy condition.

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Oteri, Omoezime Janet, Ekene Cynthia Onukwulu, Abbey Ngochindo Igwe, Chikezie Paul-Mikki Ewim, Augustine Ifeanyi Ibeh, and Adedamola Sobowale. "Dynamic Pricing Models for Logistics Product Management: Balancing Cost Efficiency and Market Demands." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 1 (2023): 861–69. https://doi.org/10.54660/.ijmrge.2023.4.1-861-869.

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Dynamic pricing models for logistics product management are increasingly vital in optimizing cost efficiency while addressing fluctuating market demands. These models leverage real-time data, algorithms, and market trends to adjust prices dynamically, ensuring that logistics providers can balance profitability with customer expectations. By considering factors such as transportation costs, demand volatility, inventory levels, and competitive pricing strategies, dynamic pricing helps improve operational efficiency and market responsiveness. The integration of machine learning and predictive ana

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Jia, Zhining, Qi Chen, and Qi Xu. "The Art of Balancing Price and Plug: Developing a Theoretical Model for Dynamic Pricing in the Electric Vehicle Market." Sustainability 16, no. 21 (2024): 9325. http://dx.doi.org/10.3390/su16219325.

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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 pr

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Awais, Muhammad. "Optimizing Dynamic Pricing through AI-Powered Real-Time Analytics: The Influence of Customer Behavior and Market Competition." Qlantic Journal of Social Sciences 5, no. 3 (2024): 99–108. http://dx.doi.org/10.55737/qjss.370771519.

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This study explores the role of AI-powered real-time analytics in enhancing the effectiveness of dynamic pricing strategies within competitive markets. Leveraging the growing relevance of artificial intelligence in business operations, the research investigates the direct impact of AI on pricing outcomes while assessing the moderating effects of customer behaviour and market competition intensity. Drawing on quantitative analysis, the study reveals that AI-driven pricing significantly improves pricing effectiveness, especially in highly competitive industries. However, price sensitivity among

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Shaulska, Larysa, Petro Pererva, Oleksandra Kosenko, Lesia Marchuk, and Oleksandr Hrechanyi. "PRICING MODELS FOR INNOVATIVE PRODUCTS IN THE SYSTEM OF ECONOMIC ASSESSMENT OF COMPETITIVENESS OF E-COMMERCE ENTERPRISES." Bulletin of the National Technical University "Kharkiv Polytechnic Institute" (economic sciences), no. 1 (February 17, 2024): 130–36. https://doi.org/10.20998/2519-4461.2024.1.130.

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The article analyzes the existing pricing models for innovative products in the context of economic assessment of the competitiveness of e-commerce enterprises. Particular attention is paid to adapting pricing strategies to rapidly changing market conditions, digital platforms, and consumer preferences. Modern models of pricing for innovative products are studied in the context of economic assessment of the competitiveness of e-commerce enterprises. Theoretical approaches to setting prices for innovative goods, in particular models of cost, competitive, consumer and dynamic pricing, have been

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Oteri, Omoezime Janet, Ekene Cynthia Onukwulu, Abbey Ngochindo Igwe, Chikezie Paul-Mikki Ewim, Augustine Ifeanyi Ibeh, and Adedamola Sobowale. "Artificial Intelligence in Product Pricing and Revenue Optimization: Leveraging Data-Driven Decision-Making." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 1 (2023): 842–51. https://doi.org/10.54660/.ijmrge.2023.4.1-842-851.

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Artificial Intelligence (AI) has emerged as a transformative tool in product pricing and revenue optimization, offering businesses a data-driven approach to decision-making. By analyzing vast amounts of historical and real-time data, AI models can identify patterns and predict consumer behavior, enabling dynamic pricing strategies that optimize revenue and improve competitiveness. Machine learning algorithms, in particular, facilitate the continuous adaptation of pricing models to market changes, competitor actions, and consumer demand fluctuations. This technology not only enhances pricing pr

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Polacek, Lukas, Milos Ulman, Petr Cihelka, and Edita Silerova. "Dynamic Pricing in E-commerce: Bibliometric Analysis." Acta Informatica Pragensia 13, no. 1 (2024): 114–33. https://doi.org/10.18267/j.aip.227.

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The paper is designed to present the development of scientific research into dynamic pricing in the e -commerce industry. Researchers all over the world attempt to investigate the influence of dynamic pricing on revenue and operational costs and propose models to solve operational or strategic problems in different industries, including e -commerce. In order to understand the level of development of dynamic pricing in e -commerce, a bibliometric analysis is performed. The analysis covers 153 papers collected from the Web of Science database. The analysis reveals that while dynamic pricing rese

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Doffou, Ako. "Testing derivatives pricing models under higher-order moment swaps." Studies in Economics and Finance 36, no. 2 (2019): 154–67. http://dx.doi.org/10.1108/sef-04-2018-0106.

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Purpose This paper aims to test three parametric models in pricing and hedging higher-order moment swaps. Using vanilla option prices from the volatility surface of the Euro Stoxx 50 Index, the paper shows that the pricing accuracy of these models is very satisfactory under four different pricing error functions. The result is that taking a position in a third moment swap considerably improves the performance of the standard hedge of a variance swap based on a static position in the log-contract and a dynamic trading strategy. The position in the third moment swap is taken by running a Monte C

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Neussner, Wolfgang, Daniel Ebner, and Maximilian Lackner. "Value Creation by Dynamic Pricing through Digitization and Industry-Wide Perspective." International Journal of Economics and Finance 14, no. 1 (2021): 115. http://dx.doi.org/10.5539/ijef.v14n1p115.

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Dynamic Pricing (DP), also known as surge pricing or dynamic price management, is the adjustment of prices for goods and services depending on the current market situation. Its purpose is to maximize profit, and the practice is getting more and more common. Dynamic pricing was first spotted in online retail; Also in offline retail, it can be found, e.g. as electronic price tags, as well as in on-demand services in mobility and smart meters in the energy industry. Dynamic pricing offers opportunities for vendors. The goal of this paper is to examine the current status and the new opportunities

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Lombardi, Claudio, Luís Picado-Santos, and Anuradha M. Annaswamy. "Model-Based Dynamic Toll Pricing: An Overview." Applied Sciences 11, no. 11 (2021): 4778. http://dx.doi.org/10.3390/app11114778.

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In this paper, we review some of the most recent research regarding design, simulation, implementation and evaluation of dynamic tolling schemes. Analyzing the structure of the reviewed studies, we identify the common elements and the differences in the approaches chosen by different authors, presenting an overview of the methods for price definition and of the simulation techniques as well as a discussion on the newest technology applications in the field. Optimization revealed to be the dominant price definition method, while control-based algorithms are notably employed for managed lanes to

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Ding, Hanzhi. "Research on option pricing of Tesla based on Black-Scholes-Merton, Binomial Tree, and Cox-Ingersoll-Ross Models." Highlights in Business, Economics and Management 22 (December 27, 2023): 35–40. http://dx.doi.org/10.54097/mwd4s763.

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This study examines the predictive accuracy of three option pricing models—Black-Scholes-Merton (BSM), Binomial Tree, and Cox-Ingersoll-Ross (CIR)—using Tesla Inc's real data. The research surveys the evolution of option pricing models and their significance in risk management and speculation. By applying these models to Tesla's volatile stock, the paper compares their predictions against actual prices. Results indicate that while outcome yielded from all three models resembles the real price to a reasonable extent, the Binomial Tree model's superior performance offers closer mean values and c

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Collins, Andrew, and Lyn Thomas. "Learning competitive dynamic airline pricing under different customer models." Journal of Revenue and Pricing Management 12, no. 5 (2013): 416–30. http://dx.doi.org/10.1057/rpm.2013.10.

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Dotsey, Michael, and Robert G. King. "Implications of state-dependent pricing for dynamic macroeconomic models." Journal of Monetary Economics 52, no. 1 (2005): 213–42. http://dx.doi.org/10.1016/j.jmoneco.2004.10.004.

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Schlosser, Rainer. "Stochastic dynamic pricing and advertising in isoelastic oligopoly models." European Journal of Operational Research 259, no. 3 (2017): 1144–55. http://dx.doi.org/10.1016/j.ejor.2016.11.021.

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Wu, Xiang, Yeming (Yale) Gong, Haoxuan Xu, Chengbin Chu, and Jinlong Zhang. "Dynamic lot-sizing models with pricing for new products." European Journal of Operational Research 260, no. 1 (2017): 81–92. http://dx.doi.org/10.1016/j.ejor.2016.12.008.

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Hajji, Adnéne, Robert Pellerin, Pierre-Majorique Léger, Ali Gharbi, and Gilbert Babin. "Dynamic pricing models for ERP systems under network externality." International Journal of Production Economics 135, no. 2 (2012): 708–15. http://dx.doi.org/10.1016/j.ijpe.2011.10.004.

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Qureshi, Mohammed Naeem, Nor Azlinah Md. Lazam, Shadia Baroud, and Alaa Mahmoud Ali Barhoom. "Optimisation and Cross-Validation of Hybrid Pricing Algorithm using SL’s CART Model for E-Hailing." Journal of Advanced Research in Applied Sciences and Engineering Technology 54, no. 1 (2024): 91–108. https://doi.org/10.37934/araset.54.1.91108.

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Abstract:

Machine learning (ML) transforms the world and creates new technological avenues. The ML models such as supervised, unsupervised, and reinforcement learning (RL) offer various simple, medium, and complex solutions to the real world. Banking, transportation, and e-commerce rely on ML models. The e-hailing falls under the transport industry which relies heavily on ML’s RL model to build its dynamic pricing (DP) strategy. However, associated pricing issues due to limitations of the RL model could potentially jeopardize e-hailing grandeur and impact the revolutionary sector. The DP assists e-haili

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Li, Tao, Yan Chen, and Taoying Li. "Pricing Strategies of Logistics Distribution Services for Perishable Commodities." Algorithms 11, no. 11 (2018): 186. http://dx.doi.org/10.3390/a11110186.

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The problem of pricing distribution services is challenging due to the loss in value of product during its distribution process. Four logistics service pricing strategies are constructed in this study, including fixed pricing model, fixed pricing model with time constraints, dynamic pricing model, and dynamic pricing model with time constraints in combination with factors, such as the distribution time, customer satisfaction, optimal pricing, etc. By analyzing the relationship between optimal pricing and key parameters (such as the value of the decay index, the satisfaction of consumers, dispa

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Chen, Qin, and Komla Agbenyo Folly. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review." Energies 16, no. 1 (2022): 146. http://dx.doi.org/10.3390/en16010146.

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The high penetration of electric vehicles (EVs) will burden the existing power delivery infrastructure if their charging and discharging are not adequately coordinated. Dynamic pricing is a special form of demand response that can encourage EV owners to participate in scheduling programs. Therefore, EV charging and discharging scheduling and its dynamic pricing model are important fields of study. Many researchers have focused on artificial intelligence-based EV charging demand forecasting and scheduling models and suggested that artificial intelligence techniques perform better than conventio

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Rakhimova, Guzel S., and Almaz R. Galyaviyev. "ECONOMICS OF DIGITAL PLATFORMS: PRICING AND MONETIZATION MODELS UNDER NETWORK EFFECTS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 5/8, no. 158 (2025): 204–12. https://doi.org/10.36871/ek.up.p.r.2025.05.08.022.

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Digital platforms are reshaping the architecture of economic interaction, where classical pricing and monetization models are being replaced by flexible, multilayered strategies driven by network effects. This article explores pricing mechanisms on two- and multi-sided markets, analyzing behavioral and structural foundations of platform economics. It highlights how network effects influence participation costs, price discrimination, and ecosystem development. Key monetization models are examined, including transactional, subscription-based, commission-based, and data-driven models. Examples of

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