Hybrid Evolutionary–Optimization Methods for Furnace Design Decisions in Glass Container Industry

Hybrid Evolutionary–Optimization Methods for Furnace Design Decisions in Glass Container Industry | 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 Hybrid Evolutionary–Optimization Methods for Furnace Design Decisions in Glass Container Industry Flaviana Amorim, Magna Paulina de Souza Ferreira, Marcio da Silva Arantes, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8187087/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This paper studies the Glass Container Industry Problem regarding a New Furnace (GCIP--NF), in which a plant must decide the melting capacity of a new furnace and the set of moulding machines. We propose a mixed-integer linear programming (MILP) model that captures furnace capacity, machine configuration and demand satisfaction. The binary design variables are evolved by genetic-algorithm variants (simple and multi-population schemes), while a linear relaxation is solved for the continuous variables. A Greedy Filter heuristic discards infeasible configurations and provides warm starts for the linear subproblems, yielding hybrid matheuristics. The methods are evaluated on 200 instances generated from data and grouped into small and large cases. For small instances, Branch-and-Cut finds optimal solutions and smaller optimality gaps, whereas Greedy Filter–enhanced genetic algorithms produce near-optimal solutions with lower computational times. For large instances, the exact method often fails to find feasible solutions within the time limit, while genetic algorithms yield similar objective values; Greedy Filter–based variants achieve the best time performance. A 10-year Return on Equity (ROE) analysis shows that the choice of furnace–machine configuration can produce differences above 1.3 percentage points per year, underscoring the impact of the proposed approaches. Genetic Algorithms Glass Container Industry Mathematical Modeling Production Planning Matheuristic Full Text Cite Share Download PDF Status: Published Journal Publication published 29 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 30 Jan, 2026 Reviewers agreed at journal 09 Dec, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 25 Nov, 2025 First submitted to journal 23 Nov, 2025 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. 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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-8187087","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550626775,"identity":"4550fb26-f34e-4f9f-9c1b-0298b38859ac","order_by":0,"name":"Flaviana Amorim","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Flaviana","middleName":"","lastName":"Amorim","suffix":""},{"id":550626776,"identity":"725bc4d3-35b2-4ca3-81d0-b8ac76b37699","order_by":1,"name":"Magna Paulina de Souza 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