A Computational Optimization Framework for Dynamic Pricing in E-Commerce Using Integrated Forecasting and Learning Algorithms

A Computational Optimization Framework for Dynamic Pricing in E-Commerce Using Integrated Forecasting and Learning Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Computational Optimization Framework for Dynamic Pricing in E-Commerce Using Integrated Forecasting and Learning Algorithms PREMA KUMARI, Antony Raj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8986898/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract This paper proposes a computational optimization framework for dynamic pricing in e-commerce by integrating time-series forecasting and adaptive learning mechanisms within a constrained stochastic optimization model. Demand is estimated using an ARIMA-based forecasting module and incorporated into a rolling-horizon revenue maximization problem under inventory and price constraints. A gradient-based adaptive update rule dynamically adjusts prices in response to observed demand. Closed-form optimality conditions are derived, convergence properties are established, and computational complexity is analyzed. Sensitivity analysis demonstrates robustness with respect to elasticity, demand uncertainty, and inventory levels. The framework offers a scalable computational solution for real-time revenue management. Dynamic Pricing Computational Optimization Forecasting Stochastic Programming Revenue Management Sensitivity Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Apr, 2026 Reviewers invited by journal 11 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 27 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8986898","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604591583,"identity":"97dabf51-c85c-4d67-be3c-5416e110ea87","order_by":0,"name":"PREMA KUMARI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYPCCBBDBxpBwwAZIMzYeIFILM0hLGkhLAwlaGA4cBnPxajGXPnx0ww+GNHn+2eePPXhw5rzd2vbDQFtqbKJxabHsS0u72cOQYzjjXDK7QcKN28nbziQCtRxLy23AocXgDI/ZDR6GCsaGM8xsEgkfbiebHQBqYWw4jEcL/7ebfxgq7OdDtJxLNjv/kJAWHrbbPAw5iRvAWm4csDO7QcAWyx42s9syBmnJG88wm0kknElOMLsBtCUBj1/MeZif3XxTkWw77wzjM8kfx+zszc6nP3zwocYGt8OQSDBIBKtMwKEcTTEE2ONRPApGwSgYBSMUAABpuGXrnrT3hgAAAABJRU5ErkJggg==","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"PREMA","middleName":"","lastName":"KUMARI","suffix":""},{"id":604591584,"identity":"4be7d99f-6a5c-4349-af64-91c1a4864fca","order_by":1,"name":"Antony Raj","email":"","orcid":"","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Antony","middleName":"","lastName":"Raj","suffix":""}],"badges":[],"createdAt":"2026-02-27 10:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8986898/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8986898/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104721841,"identity":"74a67012-e462-4aac-8589-d23c4c8dab24","added_by":"auto","created_at":"2026-03-16 12:28:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":352002,"visible":true,"origin":"","legend":"","description":"","filename":"CMSmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8986898/v1_covered_1ebcdbec-06f2-43e0-9fc9-3e6bf128a3a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Computational Optimization Framework for Dynamic Pricing in E-Commerce Using Integrated Forecasting and Learning Algorithms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"computational-management-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Computational Management Science](https://www.springer.com/journal/10287)","snPcode":"10287","submissionUrl":"https://submission.nature.com/new-submission/10287/3","title":"Computational Management Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dynamic Pricing, Computational Optimization, Forecasting, Stochastic Programming, Revenue Management, Sensitivity Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8986898/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8986898/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper proposes a computational optimization framework for dynamic pricing in e-commerce by integrating time-series forecasting and adaptive learning mechanisms within a constrained stochastic optimization model. 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