Szczegóły publikacji
Opis bibliograficzny
Consumer transactions simulation through generative adversarial networks under stock constraints in large-scale retail / Sergiy Tkachuk, Szymon ŁUKASIK, Anna Wróblewska // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2025 — vol. 14 iss. 2 art. no. 284, s. 1–14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–14, Abstr. — Publikacja dostępna online od: 2025-01-12. — Sz. Łukasik - dod. afiliacja: Systems Research Institute, Polish Academy of Sciences, Warsaw
Autorzy (3)
- Tkachuk Sergiy
- AGHŁukasik Szymon
- Wróblewska Anna
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164031 |
|---|---|
| Data dodania do BaDAP | 2025-11-18 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics14020284 |
| Rok publikacji | 2025 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecasting and fine-tune inventory management. This paper presents an innovative application of Generative Adversarial Networks (GANs) to generate synthetic retail transaction data, specifically focusing on a novel system architecture that combines consumer behavior modeling with stock-keeping unit (SKU) availability constraints to address real-world assortment optimization challenges. We diverge from conventional methodologies by integrating SKU data into our GAN architecture and using more sophisticated embedding methods (e.g., hyper-graphs). This design choice enables our system to generate not only simulated consumer purchase behaviors but also reflects the dynamic interplay between consumer behavior and SKU availability—an aspect often overlooked, among others, because of data scarcity in legacy retail simulation models. Our GAN model generates transactions under stock constraints, pioneering a resourceful experimental system with practical implications for real-world retail operation and strategy. Preliminary results demonstrate enhanced realism in simulated transactions measured by comparing generated items with real ones using methods employed earlier in related studies. This underscores the potential for more accurate predictive modeling.
