A novel dynamic route optimization method and its implementation using Python to optimize ship voyages sailing time based on weather routing techniques

A novel dynamic route optimization method and its implementation using Python to optimize ship voyages sailing time based on weather routing techniques | 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 novel dynamic route optimization method and its implementation using Python to optimize ship voyages sailing time based on weather routing techniques Ahmad A. Moussa, Amman A. Ali, Mohi Eldeen El sayeh, Ahmad S. Shehata This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5707487/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 Energy conservation, emission reduction, and voyage time optimization are critical concerns in the marine sector, where enhancing ship energy efficiency and sailing duration is essential for significant decreases in energy consumption and CO2 emissions. Existing studies generally focus on either sailing speed or route optimization, sometimes overlooking their interplay under continuously variable weather conditions, hence reducing the precision and quality of proposed solutions. This research presents an advanced Time Boundary Semicircles (TBS) Algorithm, which provides a robust and precise optimization model that incorporates involuntary speed reduction based on real-time weather data from the Copernicus Marine Environment Monitoring Service (CMEMS), subject to deterministic time boundaries. The TBS algorithm employs mathematical modeling to compute speed reductions dynamically and integrates Python libraries such as NumPy, Pandas, Matplotlib, and Cartopy to enhance waypoint optimization and software interactivity. This research compares the TBS algorithm to the SIMROUTE software, demonstrating its superior effectiveness in heavy weather scenarios with sailing time reductions ranging from 7–27.25%. Case studies validate the algorithm's efficacy through comprehensive simulation outputs, including map plots and detailed reports. They also highlight its potential for broader applications in aviation and land transport for route optimization. By bridging the gaps in maritime routing optimization, the TBS algorithm represents a significant advancement in sustainable and efficient transportation. Ocean Engineering TBS algorithm ship weather routing Sailing time: Path planning problem Dynamic route optimization Divide and Conquer algorithm Greedy algorithm Python CMEMS Full Text Additional Declarations The authors declare no competing interests. Supplementary Files CasablancaLisbonTBSVs.SIMROUTE.mp4 Explaination video 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-5707487","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":394170630,"identity":"c4854d76-a0a4-4696-8697-1b07a0310c22","order_by":0,"name":"Ahmad A. 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Existing studies generally focus on either sailing speed or route optimization, sometimes overlooking their interplay under continuously variable weather conditions, hence reducing the precision and quality of proposed solutions. This research presents an advanced Time Boundary Semicircles (TBS) Algorithm, which provides a robust and precise optimization model that incorporates involuntary speed reduction based on real-time weather data from the Copernicus Marine Environment Monitoring Service (CMEMS), subject to deterministic time boundaries.\u003c/p\u003e \u003cp\u003eThe TBS algorithm employs mathematical modeling to compute speed reductions dynamically and integrates Python libraries such as NumPy, Pandas, Matplotlib, and Cartopy to enhance waypoint optimization and software interactivity. This research compares the TBS algorithm to the SIMROUTE software, demonstrating its superior effectiveness in heavy weather scenarios with sailing time reductions ranging from 7\u0026ndash;27.25%. Case studies validate the algorithm's efficacy through comprehensive simulation outputs, including map plots and detailed reports. They also highlight its potential for broader applications in aviation and land transport for route optimization. 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