Agent Optimization for “Open Platform for Innovation in Logistics“ (OPIL) | 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 Case Report Agent Optimization for “Open Platform for Innovation in Logistics“ (OPIL) Ladislav Körösi, František Duchoň This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4393719/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 This manuscript presents the development of an innovative module intended for integration into the Open Platform for Innovation in Logistics (OPIL) library, which plays a pivotal role in the European Union Horizon 2020 initiative, codenamed “Grow your manufacturing business“ (commonly referred to as Better Factory). The primary aim of this module is to perform efficient computations to determine the optimal number of agents required for material transportation within logistics operations. This task holds considerable importance, particularly during the nascent stages of logistics automation or subsequent phases of expansion. The recently developed module has been designed as an autonomous Docker image. To demonstrate the effectiveness of the proposed approach, this manuscript provides two examples. The first example showcases the module’s application in logistics. The second example demonstrates the module’s broader scope by applying it to an inspection task. These examples serve to underscore the versatility and practicality of the newly created module in addressing diverse challenges within logistics and beyond. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialTables.pdf 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. 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