Courier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approach
dc.authorscopusid | 59377586400 | |
dc.authorscopusid | 57217492549 | |
dc.authorscopusid | 58824446500 | |
dc.authorscopusid | 22949783700 | |
dc.contributor.author | Cay, Ahmet | |
dc.contributor.author | Kup, Eyup Tolunay | |
dc.contributor.author | Bayram, Baris | |
dc.contributor.author | Ciltik, Ali | |
dc.date.accessioned | 2024-11-15T17:48:59Z | |
dc.date.available | 2024-11-15T17:48:59Z | |
dc.date.issued | 2024 | |
dc.department | Kadir Has University | en_US |
dc.department-temp | [Cay, Ahmet; Kup, Eyup Tolunay; Bayram, Baris; Ciltik, Ali] HepsiJet, Istanbul, Turkiye; [Kup, Eyup Tolunay] Kadir Has Univ, Istanbul, Turkiye; [Cay, Ahmet] Tech Univ Munich, Munich, Germany | en_US |
dc.description.abstract | In the rapidly growing sector of crowdsourced e-commerce logistics, where delivery volumes are highly variable, the effective management of courier payouts becomes essential to maintain operational efficiency. This paper introduces a comprehensive hybrid approach, blending clustering methods with multiple advanced regression models, to accurately predict daily courier payout cash-flows. By utilizing real-world data from e-commerce operations, our methodology estimates the daily financial outflows for courier payments, a critical component for adapting to the dynamic and unpredictable nature of crowdsourced logistics. Our approach includes a thorough comparative analysis of several stateof- the-art regression models - namely, XGBoost Regressor, LightGBM Regressor, and Facebook's PROPHET - in conjunction with clustering techniques that categorize similar cross-docks based on distinct characteristics. This integrated, hybrid strategy aims to provide precise daily financial predictions for each cross-dock, which is crucial for robust financial planning and effective resource allocation. The practical implications of this research are significant, offering logistics companies a powerful tool to navigate the complexities of e-commerce environments. By ensuring more accurate cash-flow predictions, companies can optimize their operations, reduce financial uncertainties, and improve overall service quality in the highly competitive and fast-paced world of e-commerce logistics. | en_US |
dc.description.sponsorship | Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK | en_US |
dc.description.sponsorship | TUBITAK; HepsiJet | en_US |
dc.description.sponsorship | Supported by TUBITAK and HepsiJet. | en_US |
dc.description.woscitationindex | Conference Proceedings Citation Index - Science | |
dc.identifier.citationcount | 0 | |
dc.identifier.doi | 10.1007/978-3-031-67195-1_25 | |
dc.identifier.endpage | 207 | en_US |
dc.identifier.isbn | 9783031671944 | |
dc.identifier.isbn | 9783031671951 | |
dc.identifier.issn | 2367-3370 | |
dc.identifier.issn | 2367-3389 | |
dc.identifier.scopus | 2-s2.0-85206992927 | |
dc.identifier.scopusquality | Q4 | |
dc.identifier.startpage | 195 | en_US |
dc.identifier.uri | https://doi.org/10.1007/978-3-031-67195-1_25 | |
dc.identifier.volume | 1089 | en_US |
dc.identifier.wos | WOS:001329232000025 | |
dc.identifier.wosquality | N/A | |
dc.language.iso | en | en_US |
dc.publisher | Springer international Publishing Ag | en_US |
dc.relation.ispartof | International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 16-18, 2024 -- Istanbul Tech Univ, Canakkale, TURKEY | en_US |
dc.relation.ispartofseries | Lecture Notes in Networks and Systems | |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.scopus.citedbyCount | 1 | |
dc.subject | E-Commerce Logistics | en_US |
dc.subject | Crowdsourcing | en_US |
dc.subject | Payout Prediction | en_US |
dc.subject | Regression | en_US |
dc.subject | Clustering | en_US |
dc.subject | XGBoost | en_US |
dc.subject | LightGBM | en_US |
dc.subject | Prophet | en_US |
dc.subject | Machine Learning | en_US |
dc.title | Courier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approach | en_US |
dc.type | Conference Object | en_US |
dc.wos.citedbyCount | 1 | |
dspace.entity.type | Publication |