QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers

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QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers | 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 Article QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers Vasanthamani k, Pavai Madheswari S, Suganthi P This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7357463/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 The Geo/G/1 retrial queue with customers who are impatient and aware of their priorities depicts a single-server system in discrete time where arrivals follow a geometric distribution. Customers who see that to server is busy may try again after a random amount of time or leave to site if they are impatient. Priority mechanisms make sure that consumers get better service, whereas low-priority ones could have to wait longer or lose more customers. This model includes real-world situations like call centres or cloud services that have retry logic, service-level agreements, and customers who are impatient. This research introduces an innovative optimisation framework utilising to Quail Optimisation Algorithm (QOA) to improve performance in discrete-time retrial queue systems incorporating repair and vacation methods. To suggested QOA-Optimized model is designed to mitigate performance degradation in conventional Geo/G/1 and fixed-parameter retrial queues, which frequently experience significant delays, low utilisation, and ineffective retrial management. To system is checked against important metrics such mean delay (W), system utilisation (ρ), blockage probability (B), server idle probability, retry frequency (RF), mean orbit size (E[QI]), and vacation/repair-induced delay. QOA-based optimisation automatically adjusts important parameters (r, θ, φ, λ) to keep to system stable and reduce traffic. To proposed model is better than to others, as shown by to simulation results. In particular, to QOA-Optimized system had a mean delay that was 5.1 units shorter than to classical model's mean delay of 9.3 units and to fixed-parameter model's mean delay of 17.8 units. System use went up to 0.93, which is higher than to benchmarks of 0.71 and 0.86. To model also cut to chance of blocking to 0.13, to chance of being idle to 0.07, and to delay in vacation to 1.2 units. To mean orbit size also went down to 3.17, and to retry frequency went down a lot to 3.2. These changes show that QOA is good at balancing system load, cutting down on resource waste, and making to whole queuing process more efficient. framework has a lot of promise for use in service-oriented contexts that need to work in real time. Physical sciences/Engineering Physical sciences/Mathematics and computing Geo/G/1 retrial queue Quail Optimization Algorithm Blocking probability Priority aware customer Dropout Rates Full Text Additional Declarations Competing interest reported. As per through our knowledge entire research article is mine 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-7357463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":520227544,"identity":"5ed9c18c-ecbe-4857-9c17-4a640e28c17f","order_by":0,"name":"Vasanthamani 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As per through our knowledge entire research article is mine","formattedTitle":"QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Geo/G/1 retrial queue, Quail Optimization Algorithm, Blocking probability, Priority aware customer, Dropout Rates","lastPublishedDoi":"10.21203/rs.3.rs-7357463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7357463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Geo/G/1 retrial queue with customers who are impatient and aware of their priorities depicts a single-server system in discrete time where arrivals follow a geometric distribution. Customers who see that to server is busy may try again after a random amount of time or leave to site if they are impatient. Priority mechanisms make sure that consumers get better service, whereas low-priority ones could have to wait longer or lose more customers. This model includes real-world situations like call centres or cloud services that have retry logic, service-level agreements, and customers who are impatient. This research introduces an innovative optimisation framework utilising to Quail Optimisation Algorithm (QOA) to improve performance in discrete-time retrial queue systems incorporating repair and vacation methods. To suggested QOA-Optimized model is designed to mitigate performance degradation in conventional Geo/G/1 and fixed-parameter retrial queues, which frequently experience significant delays, low utilisation, and ineffective retrial management. To system is checked against important metrics such mean delay (W), system utilisation (ρ), blockage probability (B), server idle probability, retry frequency (RF), mean orbit size (E[QI]), and vacation/repair-induced delay. QOA-based optimisation automatically adjusts important parameters (r, θ, φ, λ) to keep to system stable and reduce traffic. To proposed model is better than to others, as shown by to simulation results. In particular, to QOA-Optimized system had a mean delay that was 5.1 units shorter than to classical model's mean delay of 9.3 units and to fixed-parameter model's mean delay of 17.8 units. System use went up to 0.93, which is higher than to benchmarks of 0.71 and 0.86. To model also cut to chance of blocking to 0.13, to chance of being idle to 0.07, and to delay in vacation to 1.2 units. To mean orbit size also went down to 3.17, and to retry frequency went down a lot to 3.2. These changes show that QOA is good at balancing system load, cutting down on resource waste, and making to whole queuing process more efficient. framework has a lot of promise for use in service-oriented contexts that need to work in real time.\u003c/p\u003e","manuscriptTitle":"QOA-RetrailNet: A Quail Optimization-Driven Framework for Geo/G/1 Retrial Queues with Impatient and Priority-Aware Customers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 17:23:51","doi":"10.21203/rs.3.rs-7357463/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"81d1e59c-08c2-46d4-8cd8-ddc4ad3eeac2","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55297065,"name":"Physical sciences/Engineering"},{"id":55297066,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-11-26T04:39:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-25 17:23:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7357463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7357463","identity":"rs-7357463","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

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