A robust omnichannel pricing and ordering optimization approach with return policies based on data-driven support vector clustering

DOI10.1016/j.ejor.2022.07.029OpenAlexW4288740662MaRDI QIDQ2103032

Minghe Sun, Ruozhen Qiu, Lin Ma

Publication date: 12 December 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2022.07.029

zbMATH Keywords

pricingdemand uncertaintyreturn policiesdata-driven robust optimizationomnichannel retailing

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

• Unnamed Item
• Robust discrete optimization and network flows
• Data-driven robust optimization
• When to introduce an online channel, and offer money back guarantees and personalized pricing?
• Optimizing (\(s, S\)) policies for multi-period inventory models with demand distribution uncertainty: robust dynamic programing approaches
• Managing a dual-channel supply chain under price and delivery-time dependent stochastic demand
• Forays into omnichannel: an online retailer's strategies for managing product returns
• Omnichannel inventory models accounting for buy-online-return-to-store service and random demand
• Returns freight insurance policy and the impact from a BOPS retailer
• Robust multi-product inventory optimization under support vector clustering-based data-driven demand uncertainty set
• Omnichannel retail move in a dual-channel supply chain
• Distribution network deployment for omnichannel retailing
• Pricing and the Newsvendor Problem: A Review with Extensions
• Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming

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