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Relevant bibliographies by topics / Price optimization

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Academic literature on the topic 'Price optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Price optimization"

1

Feng, Yan, and Shulin Liu. "Research on Price Formation Based on Resource Optimization Allocation." Sustainability 16, no. 12 (2024): 5129. http://dx.doi.org/10.3390/su16125129.

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Global economic growth has weakened under the guidance of equilibrium price theory, which focuses on price competition. Therefore, developing a price formation mechanism that aligns with sustainable economic development is imperative. Based on a scientific definition of the boundaries of the constituent factors of wealth creation, this paper proposes a price formation mechanism centered on resource optimization allocation and constructs a price measurement model using the Leontief input–output analysis method. To test the price formation mechanism, this paper uses panel data from 31 provinces

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Xu, Tianyou. "Optimization and Improvement of Recurrent Neural Networks in Stock Price Prediction." Frontiers in Business, Economics and Management 13, no. 3 (2024): 82–84. http://dx.doi.org/10.54097/pssmrv64.

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Recurrent Neural Networks (RNNs) are a significant branch of deep learning, particularly suitable for handling data with temporal dependencies. In the financial domain, stock price prediction is a highly focused problem. Traditional stock price prediction methods, often based on linear models, fail to fully capture the nonlinear dynamics of stock prices. RNNs, with their memory capabilities, can capture the long-term dependencies of stock prices, hence offering great application potential in stock price prediction. However, RNNs also face challenges when processing stock price data, as stock p

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Li, Hongmin, Scott Webster, and Gwangjae Yu. "Product Design Under Multinomial Logit Choices: Optimization of Quality and Prices in an Evolving Product Line." Manufacturing & Service Operations Management 22, no. 5 (2020): 1011–25. http://dx.doi.org/10.1287/msom.2019.0788.

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Problem definition: We study a product-line design problem in which customer choice among multiple products is given by a multinomial logit (MNL) model. A firm determines product quality and prices in an evolving product line to maximize profit. In particular, given the prices and quality of products that already exist in a product line, the firm optimizes prices and/or quality of the new products. Academic/practical relevance: We extend the literature on discrete choice models to include the interaction between product quality and product price and consider two variations of the problem, each

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Bondarenko, Maksym, and Victor Bondarenko. "Calibration of Dupire local volatility model using genetic algorithm of optimization." Neuro-Fuzzy Modeling Techniques in Economics 7, no. 1 (2019): 1–20. http://dx.doi.org/10.21511/nfmte.7.2018.01.

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The problem of calibration of local volatility model of Dupire has been formalized. It uses genetic algorithm as alternative to regularization approach with further application of gradient descent algorithm. Components that solve Dupire’s partial differential equation that represents dynamics of underlying asset’s price within Dupire model have been built. This price depends in particular on values of volatility parameters. Local volatility is parametrized in two dimensions (by Dupire model): time to maturity of the option and strike price (execution price). On maturity axis linear interpolati

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Faridah, Nur, and Bambang Sugiantoro. "Analisis Optimasi Pada Algoritma Long ShortTerm Memory Untuk Memprediksi Harga Saham." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 575. http://dx.doi.org/10.30865/mib.v7i1.5421.

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Stocks are the most popular financial market instrument at the moment, indicated by an increase in investors' shares of 27.15% from the previous year. The biggest risk for stock investors in investing is the risk of falling prices (capital loss) and the risk of liquidation. To minimize this risk, before investing, you should do an analysis first, one of which is by predicting stock price movements. The best method for predicting stock prices is to use Long Short Term Memory (LSTM). In order to predict optimally, it is important to select an optimization algorithm before creating a model. Of th

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Yin, Can, and Lifu Jin. "Estimating Hydrogen Price Based on Combined Machine Learning Models by 2060: Especially Comparing Regional Variations in China." Sustainability 17, no. 3 (2025): 1049. https://doi.org/10.3390/su17031049.

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Hydrogen energy’s economic efficiency is the key for China to obtain the goal of “carbon neutrality” by 2060. Different from the bottom-up methods and learning rate methods, this study estimates the hydrogen prices in China and typical regions by 2060 from the perspectives of economics and machine learning. The main factors influencing hydrogen price are determined from the perspectives of economics: hydrogen production, demand, and cost. A novel model is established based on combined machine learning models to predict hydrogen price. The hydrogen production is predicted based on the trained B

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Mulyani, Mulyani. "Stock Price Optimization through Financial Performance and Dividend Policy." KINERJA: Jurnal Manajemen Organisasi dan Industri 3, no. 2 (2024): 107–16. https://doi.org/10.37481/jmoi.v3i2.139.

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Stock price is the main indicator of a company's performance which is a major concern for investors and company management. Solid financial performance and the right dividend policy are two key factors that can affect stock prices. In a competitive market, a company must be able to optimize its stock price to attract investment and maintain shareholder trust. This study aims to identify and analyze the influence of financial performance and dividend policy on the company's stock price. By understanding this relationship, it is hoped that companies can formulate effective strategies to increase

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Supriya, Kammari, and Gandla Nagappa. "MACHINE LEARNING-BASED OPTIMIZATION OF AIRFARE PRICE FORECASTING." Cuestiones de Fisioterapia 54, no. 5 (2025): 1–9. https://doi.org/10.48047/wwg0qk10.

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Accurate airfare price forecasting is essential for both travelers and airline companies, enabling cost-effective bookings and optimized revenue management. Airfare prices are highly dynamic, influenced by factors such as seasonality, demand fluctuations, airline competition, and fuel costs. Conventional price prediction methods struggle to adapt to these complex variations, often leading to inaccurate forecasts.

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Lu, Lin Lin, Xin Ma, Ya Xuan Wang, and Gen Bo Yu. "Lead Price Forecasting Based on ARIMA Model." Advanced Materials Research 488-489 (March 2012): 1582–86. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1582.

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The time series ARIMA (3,1,4) model was established, which is taken into use of price forecasting. Then the forecasted price was applied to mining technical and economic index optimization study. Lead prices could be reliably predicted by time series ARIMA model, which had a high accuracy and the percentage of prediction error was 2.97% on average. It can solve the problems of the price data lag in the study of mine economic index optimization very well.

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Zhu, Liangji. "Optimization of linear regression in house price prediction." Applied and Computational Engineering 6, no. 1 (2023): 805–12. http://dx.doi.org/10.54254/2755-2721/6/20230928.

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House price prediction plays a very important role in housing transactions. Linear regression based algorithms show good effects in predicting house prices. They have strong interpretability and fast operation speed. However, people ignore the estimation of deviations in linear regression (LR) algorithms. In this paper, k-nearest neighbor (KNN) algorithm is supposed to estimate deviations that are added to the result of linear regression to predict house prices accurately. Furthermore, deviation regression (DR) algorithm is supposed to make the prediction result more accurate. By utilizing Bos

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Dissertations / Theses on the topic "Price optimization"

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Ekman, Sara. "Price Vector Recalculation Optimization." Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136534.

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Lee, Seonah. "Study of demand models and price optimization performance." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42914.

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Accurately representing the price-demand relationship is critical for the success of a price optimization system. This research first uses booking data from 28 U.S. hotels to investigate the validity of two key assumptions in hotel revenue management. The assumptions are: 1) customers who book later are willing to pay higher rates than customers who book earlier; and, 2) demand is stronger during the week than on the weekend. Empirical results based on an analysis of booking curves, average paid rates, and occupancy rates for group, restricted retail, unrestricted retail, and negotiated demand

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Shen, Gang. "Shadow Price Guided Genetic Algorithms." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/64.

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The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure s

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HomChaudhuri, Baisravan. "Price-Based Distributed Optimization in Large-Scale Networked Systems." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1377868426.

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Sun, Wei S. M. Massachusetts Institute of Technology. "Price of anarchy in a Bertrand oligopoly market." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/39211.

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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2006.<br>Includes bibliographical references (p. 107-110).<br>The price of anarchy quantifies the inefficiency that occurs in the total system objective in the user optimization as compared to the system optimization setting. It is well known that this inefficiency occurs due to lack of coordination among the competitors in the system. In this thesis, we study the price of anarchy in a Bertrand oligopoly market by comparing the total profits in the two settings. The main contribution of this

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Coursen, Jeffrey Thomas. "An experiment in joint product price optimization price elasticities and subsitution [sic] decisions of the hungry barfly /." Connect to this title online, 2007. http://etd.lib.clemson.edu/documents/1202498978/.

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Saeidpour, Parizy Ehsan. "Electrical Energy Retail Price Optimization for an Interconnected/Islanded Power Grid." University of Akron / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=akron1512463830323059.

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Alexis, Sara. "Combinatorial and price efficient optimization of the underlying assets in basket options." Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-204861.

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The purpose of this thesis is to develop an optimization model that chooses the optimal and price efficient combination of underlying assets for a equally weighted basket option. To obtain a price efficient combination of underlying assets a function that calculates the basket option price is needed, for further use in an optimization model. The closed-form basket option pricing is a great challenge, due to the lack of a distribution describing the augmented stochastic price process. Many types of approaches to price an basket option has been made. In this thesis, an analytical approximation o

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Cai, Mengmeng. "A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99094.

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Emerging technologies, including distributed energy resources (DERs), internet-of-things and advanced distribution management systems, are revolutionizing the power industry. They provide benefits like higher operation flexibility and lower bulk grid dependency, and are moving the modern power grid towards a decentralized, interconnected and intelligent direction. Consequently, the emphasis of the system operation management has been shifted from the supply-side to the demand-side. It calls for a reconsideration of the business model for future retail market operators. To address this need, th

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Boström, Christoffer. "Optimization of a Household Battery Storage : The Value of Load Shift." Thesis, Uppsala universitet, Fasta tillståndets fysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-298417.

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Sweden’s energy system is facing major changes in the near future in order to reducecarbon emissions and to switch to sustainable energy sources. PV systems havebecome a sensible alternative for homeowners that want to be a part of this changeand at the same time reduce the cost of their electricity bill. To further improve theutilization of their PV system and to handle the intermittent nature of solar power,battery storages have become an interesting system complement. This thesisinvestigates how batteries can provide smart services; load shift and peak price energyutilization to a household

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Books on the topic "Price optimization"

1

Alpo, Willman, and European Central Bank, eds. State-dependency and firm-level optimization: A contribution to Calvo price staggering. European Central Bank, 2007.

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Chan, Louis K. C. On portfolio optimization: Forecasting covariances and choosing the risk model. National Bureau of Economic Research, 1999.

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Newman, Mark. Models of network formation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0013.

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This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying

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Introduction to Optimization-Based Decision Making. Taylor & Francis Group, 2021.

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Miranda, Joao Luis de. Introduction to Optimization-Based Decision Making. Taylor & Francis Group, 2021.

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Miranda, Joao Luis de. Introduction to Optimization-Based Decision-Making. Taylor & Francis Group, 2021.

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MARKMAN. 2005 RUMELHART PRIZE HONORING P SMOLENSKY COG V30#5: OPTIMIZATION AND GRAMMAR. Lawrence Erlbaum, 2006.

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Jarrow, Robert A. Continuous-Time Asset Pricing Theory: A Martingale-Based Approach. Springer, 2019.

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Jarrow, Robert A. Continuous-Time Asset Pricing Theory: A Martingale-Based Approach. Springer, 2018.

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Jarrow, Robert A. Continuous-Time Asset Pricing Theory: A Martingale-Based Approach. Springer International Publishing AG, 2021.

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Book chapters on the topic "Price optimization"

1

Haugom, Erik. "Price optimization." In Essentials of Pricing Analytics. Routledge, 2020. http://dx.doi.org/10.4324/9780429345319-7.

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Nagurney, Anna. "Walrasian Price Equilibrium." In Encyclopedia of Optimization. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-54621-2_708-1.

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Nagurney, Anna. "Spatial Price Equilibrium." In Encyclopedia of Optimization. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-54621-2_628-1.

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Simon, Hermann, and Martin Fassnacht. "Decision: Long-Term Price Optimization." In Price Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99456-7_7.

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Nagurney, Anna. "Pollution Permits for Spatial Price Networks." In Applied Optimization. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-6871-8_13.

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Higbie, Jon A. "B2B Price Optimization Analytics." In Revenue Management. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230294776_10.

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Feldkord, Björn, Christine Markarian, and Friedhelm Meyer Auf der Heide. "Price Fluctuation in Online Leasing." In Combinatorial Optimization and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71147-8_2.

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Kunsch, P. L., M. Theys, A. Chevalier, and J. P. Iacopetta. "A System Dynamics Model of Stock Price Movements." In Applied Optimization. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-4919-9_11.

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Chen, Cong, Paolo Penna, and Yinfeng Xu. "Selfish Jobs with Favorite Machines: Price of Anarchy vs. Strong Price of Anarchy." In Combinatorial Optimization and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71147-8_16.

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Gallego, Guillermo, and Huseyin Topaloglu. "Competitive Assortment and Price Optimization." In International Series in Operations Research & Management Science. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9606-3_11.

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Conference papers on the topic "Price optimization"

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Nithisha, Chintham, Bindu Siva Sree Barrenkala, Chengamma Chitteti, Varshitha Kosuri, Sudheer Goura, and Harshavardhan Reddy Kota. "Crypto Predict: Price Forecast & Trading Optimization." In 2024 International Conference on Expert Clouds and Applications (ICOECA). IEEE, 2024. http://dx.doi.org/10.1109/icoeca62351.2024.00181.

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Dranko, Oleg I., and Anna S. Belova. "Price Optimization Method for Highly Technological Products." In 2025 International Russian Smart Industry Conference (SmartIndustryCon). IEEE, 2025. https://doi.org/10.1109/smartindustrycon65166.2025.10986141.

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Akkor, Ilayda, Shachit S. Iyer, John Dowdle, Le Wang, and Chrysanthos Gounaris. "Economic Optimization and Impact of Utility Costs on the Optimal Design of Piperazine-Based Carbon Capture." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.147100.

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Recent advances in process design for solvent-based, post-combustion capture (PCC) processes, such as the Piperazine/Advanced Flash Stripper (PZ/AFS) process, have led to a reduction in the energy required for capture. Even though PCC processes are progressively improving in Technology Readiness Levels (TRL), with a few commercial installations, incorporating carbon capture adds cost to any operation. Hence, cost reduction will be instrumental for proliferation. The aim of this work is to improve process economics through optimization and to identify the parameters in our economic model that h

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Laky, Daniel J., Radhakrishna Tumbalam Gooty, Tyler Jaffe, et al. "IDAES-PSE Software Tools for Optimizing Energy Systems and Market Interactions." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.137860.

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Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-economic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization wi

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Daruvuri, Rajesh, Kiran Kumar Patibandla, and Pravallika Mannem. "Data Driven Retail Price Optimization using XGBoost and Predictive Modeling." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984940.

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Chen, Fei, Zichen Wang, and Wenjun Wang. "DFT-Triangle: A Novel Frequency Spectrogram Feature for Stock Price Prediction." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS). IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704304.

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Macdonald, Ruaridh, and Dharik S. Mallapragada. "Integrated Design and Scheduling Optimization of Multi-product processes � case study of Nuclear-Based Hydrogen and Electricity Co-Production." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.158610.

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Increasing wind and solar electricity generation in power systems increases temporal variability in electricity prices which incentivizes the development of flexible processes for electricity generation and electricity-based fuels/chemicals production. Here, we develop a computational framework for the integrated design and optimization of multi-product processes interacting with the grid under time-varying electricity prices. Our analysis focuses on the case study of nuclear-based hydrogen (H2) and electricity generation, involving nuclear power plants (NPP) producing high temperature heat an

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Wijaya, Alfrethanov Christian, Sutikno, Khadijah, Retno Kusumaningrum, Aris Sugiharto, and Guruh Aryotejo. "Prophet Algorithm Optimization with Particle Swarm Optimization for Adjusted Closing Price Forecasting in Xiaomi Corporation." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636888.

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Chérot, Guénolé, Roman Le Goff Latimier, Benjamin Cajna, and Hamid Ben Ahmed. "Learning-Based Optimization of the Exchange Price in a Heterogeneous Market." In 2024 22nd International Conference on Intelligent Systems Applications to Power Systems (ISAP). IEEE, 2024. http://dx.doi.org/10.1109/isap63260.2024.10744279.

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Yabe, Akihiro, Shinji Ito, and Ryohei Fujimaki. "Robust Quadratic Programming for Price Optimization." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/648.

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The goal of price optimization is to maximize total revenue by adjusting the prices of products, on the basis of predicted sales numbers that are functions of pricing strategies. Recent advances in demand modeling using machine learning raise a new challenge in price optimization, i.e., how to manage statistical errors in estimation. In this paper, we show that uncertainty in recently-proposed prescriptive price optimization frameworks can be represented by a matrix normal distribution. For this particular uncertainty, we propose novel robust quadratic programming algorithms for conservative l

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Reports on the topic "Price optimization"

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She, Ruifeng, and Yanfeng Ouyang. Generalized Link-Cost Function and Network Design for Dedicated Truck-Platoon Lanes to Improve Energy, Pavement Sustainability, and Traffic Efficiency. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-037.

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Recent development of autonomous and connected trucks (ACT) has provided the freight industry with the option of using truck platooning to improve fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterioration and increases the life cycle costs for the highway agency. Also, effectiveness of truck platooning can be maximized only in dedicated lanes; and its benefits and costs need to be properly balanced between stakeholders. This paper proposes a net

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Amirav, Aviv, and Steven Lehotay. Fast Analysis of Pesticide Residues in Agricultural Products. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7695851.bard.

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The overall theme of this project was to increase the speed of analysis for monitoring pesticide residues in food. Traditionally, analytical methods for multiple pesticides are time-consuming, expensive, laborious, wasteful, and ineffective to meet critical needs related to food safety. Faster and better methods were needed to provide more cost-effective detection of chemical contaminants, and thus provide a variety of benefits to agriculture. This overarching goal to speed and improve pesticide analysis was successfully accomplished even beyond what was originally proposed by the investigator

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Shpigel, Muki, Allen Place, William Koven, Oded (Odi) Zmora, Sheenan Harpaz, and Mordechai Harel. Development of Sodium Alginate Encapsulation of Diatom Concentrates as a Nutrient Delivery System to Enhance Growth and Survival of Post-Larvae Abalone. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7586480.bard.

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The major bottlenecks in rearing the highly priced gastropod abalone (Haliotis spp.) are the slow growth rate and the high mortality during the first 8 to 12 weeks following metamorphosis and settling. The most likely reason flor these problems is related to nutritional deficiencies in the diatom diet on which the post larvae (PL) feed almost exclusively in captivity. Higher survival and improved growth rate will reduce the considerable expense of hatchery-nursery resisdence time and thereflore the production costs. BARD supported our research for one year only and the support was given to us

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