Multi- Objective Green Vehicle Routing Problem with Uncertain Customer Demand and Carbon Emission Reduction

Multi- Objective Green Vehicle Routing Problem with Uncertain Customer Demand and Carbon Emission Reduction | 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 Multi- Objective Green Vehicle Routing Problem with Uncertain Customer Demand and Carbon Emission Reduction Priyanka ., Mohit Kumar Kakkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7210747/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 Vehicle Routing Problem with Time Windows is a combinatorial optimization problem that deals with the fleet of vehicles to find the optimal set of routes while serving the customers at different nodes in a given geographical region within specific time intervals. In this research article, the objective is to minimize the total operational costs, 𝐶𝑂2 emissions and fulfilling the fuzzy customer demand. To calculate the optimal results, a mathematical model consisting of all the restricted constraints is presented and genetic algorithm and particle swarm optimization algorithm is preferred to find the solutions. Moreover, alpha- cut method is applied to calculate the crisp interval for the fuzzy demands. To conclude the experimental results, Solomon dataset (R101) is used to compare the outcomes of genetic algorithm and particle swarm optimization algorithm and it is observed that both genetic algorithm and particle swarm optimization algorithm provides satisfactory results but genetic algorithm provides more optimal outcomes as compared to particle swarm optimization algorithm. Vehicle Routing Carbon Emissions Uncertainty Evolutionary Algorithm Logistics Full Text 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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