Courier Payout Cash-Flow Prediction in Crowdsourced E-Commerce Logistics: a Hybrid Machine Learning Approach
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Date
2024
Authors
Çay,A.
Küp,E.T.
Bayram,B.
Çıltık,A.
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Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
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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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Keywords
Clustering, Crowdsourcing, E-Commerce Logistics, LightGBM, Machine Learning, Payout Prediction, Prophet, Regression, XGBoost
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Citation
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N/A
Scopus Q
Q4
Source
Lecture Notes in Networks and Systems -- International Conference on Intelligent and Fuzzy Systems, INFUS 2024 -- 16 July 2024 through 18 July 2024 -- Canakkale -- 318029
Volume
1089 LNNS
Issue
Start Page
195
End Page
207