Original title: Variable Selection for Feature-Based Newsvendor
Zhaoliang Yuan, Jie Wang · September 2026
Stores must decide how much stock to order so they do not run out or waste unsold goods. They often use extra information, like weather or trends, to guess demand. But tracking too many details is expensive and confusing. Researchers tackled this by strictly limiting how many features a model can use. They designed a mathematical system that balances storage costs, shortage costs, and stability while picking only the best predictors. Because solving this puzzle exactly is computationally hard, they also created two faster shortcut methods: a randomized-rounding technique and a greedy search. Tests showed these methods match the performance of older models while relying on far fewer features, cutting costs without hurting inventory decisions.
Imagine packing a suitcase for a vacation where you can only bring a few items. You want to include everything that might be useful, but the space is limited, so you must choose carefully to avoid missing essentials or overpacking. In this paper, businesses have too much data for inventory decisions, so they select only a few key features to ensure cost-effectiveness without overwhelming the system.
Businesses can save money and make better choices by focusing on just a few important data points. This reduces the burden of collecting and analyzing excessive information.
The abstract doesn't explicitly state limitations, but it focuses on variable selection under cardinality constraints and may not address dynamic demand changes or other real-world factors beyond the scope of the study.
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Variable Selection for Feature-Based Newsvendor — Zhaoliang Yuan, Jie Wang
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.