Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets

Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets | 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 Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets Ozgu Turgut This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4258222/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lack of proper planning is pointed out as one of the main culprits behind the significant consumer-end food waste. This observation can be used to build effective solutions, especially in digitalized societies. This paper proposes a reinforcement learning (RL) based decision support unit (DSU) in order to prevent food waste specifically for household and restaurant kitchens. The proposed RL model is improved with the help of mathematical modeling. The DSU assumes a generic kitchen environment for improving three important aspects of food management: the total shopping budget, the unused amount of ingredients, and the nutrition level offered by the selected menu. The RL-based algorithm is built to generate menu recommendations in a volatile market. All the practical contributions of the proposed planning tool to “monitor”, “prevent” and “reuse” levers of sustainable food strategy are summarized in a generic kitchen example. Results indicate that the proposed DSU can save around 30% from monthly shopping costs concurrently improving unused ingredients and nutritional intake. More insights about industrial processes such as package sizing and integration with “food sharing” services are also highlighted. Offering only one extra package size option for each ingredient in a market with volatile prices can help to diminish unused amounts completely and cut total shopping costs by 68% in a planned kitchen. Furthermore, the contribution of integrating mathematical optimization results into basic reinforcement learning implementation is observed as a 40% more reduction in the unused ingredients amount especially when the volatility of the market increases due to any disruption. Artificial Intelligence and Machine Learning Operations Research Environmental Engineering Decision support systems Reinforcement learning Mathematical modeling Food waste Digitalization Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-4258222","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290561642,"identity":"a848b2f6-9590-4613-8a0d-a213f3ab5dcd","order_by":0,"name":"Ozgu Turgut","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYJADxgdAgoePOMUJIIKZ2QCkhY0ULWwSIIqgFn7+49ckGH8czpePyD9W+TXHToaNgfnhoxt4tEg2nCmTYEg4bLnxRjLbbdltyUCHsRkb5+DRYnCwJw2kxcBwBlCL5DZmoBYeNmm8Wg7zILQUS26rJ0LLMfZjYC3yEslsjB+3HSasRbKHh9kiIS3dwIDnsbE047bjPGzMBPwCDLGHNz7YWBvItyc+/PhzW7U9P3vzw8f4tADjzgAcLQYHgDHDAxJgxqscBNgfgCn5BmCK+UFQ9SgYBaNgFIxEAADIcz+RHcgfygAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7677-1184","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ozgu","middleName":"","lastName":"Turgut","suffix":""}],"badges":[],"createdAt":"2024-04-12 13:58:58","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4258222/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4258222/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54664829,"identity":"04a07905-fc27-440d-9cdd-4c9133d2b0d1","added_by":"auto","created_at":"2024-04-15 03:06:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":729130,"visible":true,"origin":"","legend":"","description":"","filename":"KitchenManagerprev.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4258222/v1_covered_da8e5750-61d7-43ff-9a8f-488be6aff60d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImproved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Decision support systems, Reinforcement learning, Mathematical modeling, Food waste, Digitalization","lastPublishedDoi":"10.21203/rs.3.rs-4258222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4258222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLack of proper planning is pointed out as one of the main culprits behind the significant consumer-end food waste. 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