Stochastic and Correlated Waste Collection Problem with Time Windows: A Simheuristic Approach

Stochastic and Correlated Waste Collection Problem with Time Windows: A Simheuristic Approach | 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 Stochastic and Correlated Waste Collection Problem with Time Windows: A Simheuristic Approach Anas Musah, Gaston Edem Awashie, Akoto Yaw Omari-Sasu, Peter Amoako-Yirenkyi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7894143/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 Urban waste collection is hindered by uncertainties and correlations in arc travel times and node waste demands, impacting efficiency and reliability. This study introduces the SCWCPTW model, a variant of SWCP, which treats these variables as correlated random variables. It uniquely accounts for correlations across multiple stochastic components, unlike previous studies that focused on single-component correlations. It incorporates covariance structures into recourse actions by applying proportional penalties to overload and time window violations. A simheuristic combining SA, LNS, and MC is developed to solve the model. Simulation results show that higher variability in stochastic components slows convergence and increases uncertainty, reflecting a more realistic nature in practice. Computational results on modified Solomon benchmarks show that, compared to the deterministic solution (DS), the uncorrelated solution (USS) showed a 6.89% higher cost and 3.73% higher demand, while the correlated solution (CSS) nearly matched DS with only a 0.001% cost difference and a 0.05% demand decrease while achieving higher reliability (0.99). Furthermore, the CSS had lower penalty costs, lower objective costs, and greater reliability (0.99) compared to USS (0.96) when analysed with correlation strength. This indicates that modelling correlations improves cost efficiency and operational robustness without significant computational overhead. Future research directions could extend the proportional penalty approaches to more dynamic penalty structures. Furthermore, the SCWPTW model could be extended to include correlations between travel times and waste demands. Discrete Mathematics Applied Mathematics Computational Mathematics Stochastic Waste Collection Demand Correlations Travel Times Correlations Simheuristics Metaheuristics Monte-Carlo Simulation Full Text Additional Declarations The authors declare no competing interests. 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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