DyCOD - Determining Cash on Delivery Limits for Real-Time E-commerce Transactions via Constrained Optimisation Modelling

Author:

Deep AkashORCID,Kattamuru Sri CharanORCID,Negi MeghanaORCID,Mathew JoseORCID,Sathyanarayana JairajORCID

Publisher

Springer Nature Switzerland

Reference17 articles.

1. Aa, G., Sb, V., Ab, M.K.R.: Deciding bank client’s credit limit and home loan using machine learning. Smart Intell. Comput. Commun. Technol. 38, 288 (2021)

2. Bashar, M.A., Nayak, R., Astin-Walmsley, K., Heath, K.: Machine learning for predicting propensity-to-pay energy bills. Intell. Syst. Appl. 17, 200176 (2023)

3. Economic-Times: Cash on delivery about \$30 billion of India’s ecommerce market (2022). https://economictimes.indiatimes.com/industry/services/retail/cash-on-delivery-about-30-billion-of-indias-ecommerce-market-says-gokwik/articleshow/96112033.cms?from=mdr

4. İç, Y.T.: Development of a credit limit allocation model for banks using an integrated fuzzy TOPSIS and linear programming. Expert Syst. Appl. 39(5), 5309–5316 (2012)

5. Licari, J., Loiseau-Aslanidi, O., Tolstova, V., Sadat, M.: Determining the optimal dynamic credit card limit (2021)

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