Courier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approach

dc.authorscopusid59377586400
dc.authorscopusid57217492549
dc.authorscopusid58824446500
dc.authorscopusid22949783700
dc.contributor.authorCay, Ahmet
dc.contributor.authorKup, Eyup Tolunay
dc.contributor.authorBayram, Baris
dc.contributor.authorCiltik, Ali
dc.date.accessioned2024-11-15T17:48:59Z
dc.date.available2024-11-15T17:48:59Z
dc.date.issued2024
dc.departmentKadir Has Universityen_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, Germanyen_US
dc.description.abstractIn 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.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKen_US
dc.description.sponsorshipTUBITAK; HepsiJeten_US
dc.description.sponsorshipSupported by TUBITAK and HepsiJet.en_US
dc.description.woscitationindexConference Proceedings Citation Index - Science
dc.identifier.citationcount0
dc.identifier.doi10.1007/978-3-031-67195-1_25
dc.identifier.endpage207en_US
dc.identifier.isbn9783031671944
dc.identifier.isbn9783031671951
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85206992927
dc.identifier.scopusqualityQ4
dc.identifier.startpage195en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-67195-1_25
dc.identifier.volume1089en_US
dc.identifier.wosWOS:001329232000025
dc.identifier.wosqualityN/A
dc.language.isoenen_US
dc.publisherSpringer international Publishing Agen_US
dc.relation.ispartofInternational Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 16-18, 2024 -- Istanbul Tech Univ, Canakkale, TURKEYen_US
dc.relation.ispartofseriesLecture Notes in Networks and Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.scopus.citedbyCount1
dc.subjectE-Commerce Logisticsen_US
dc.subjectCrowdsourcingen_US
dc.subjectPayout Predictionen_US
dc.subjectRegressionen_US
dc.subjectClusteringen_US
dc.subjectXGBoosten_US
dc.subjectLightGBMen_US
dc.subjectPropheten_US
dc.subjectMachine Learningen_US
dc.titleCourier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approachen_US
dc.typeConference Objecten_US
dc.wos.citedbyCount1
dspace.entity.typePublication

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