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New research tackles inventory control with censored demand data

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces a framework for optimizing inventory control when demand data is censored, meaning stockouts only indicate demand exceeded the stocking level. The approach utilizes a biased sample-average approximation (SAA) method to learn effective policies from this limited data. The paper proposes two algorithms, one for offline learning with near-optimal sample complexity and another for online learning that actively seeks coverage to minimize regret, offering a general principle for handling censored feedback. AI

IMPACT This research could improve inventory management systems by enabling more accurate demand forecasting and optimization with incomplete data.

RANK_REASON The item is an academic paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research tackles inventory control with censored demand data

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The item is an academic paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yuxuan Han, Xiaoyu Fan, Jiawei Zhang, Zhengyuan Zhou ·

    Towards Optimal Inventory Control under Censored Demand: A Biased Sample-Average Approximation Approach

    arXiv:2609.39397v1 Announce Type: new Abstract: We study data-driven multi-period lost-sales inventory control under censored demand, where a stockout reveals only that demand exceeded the stocking level. We develop a unified, model-based framework for policy learning from censor…

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