ISCCO: a deep learning feature extraction-based strategy framework for dynamic minimization of supply chain transportation cost losses
Yangyan Li, Tingting Chen · 2024
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Description
Online shopping floods supply chains with orders, and Yangyan Li and Tingting Chen propose a framework to stop transport costs bleeding away, in a 2024 paper. Their system, ISCCO, pairs deep learning with optimization to predict shoppers and place goods before orders land.
One part sorts customers by discount sensitivity and cancellation risk, combining an autoencoder with a random forest; classification accuracy jumped from half to nearly ninety-six percent in tests. Another part allocates stock with a genetic algorithm wrapped around an integer program, adjusting to live demand. Shipment policies tuned by the framework cut cancellations from about half of orders and lifted profit by roughly seven and a half percent at the best setting. The authors present it as a working answer to e-commerce logistics waste.
Catalog record
| CREATOR | Yangyan Li, Tingting Chen |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |