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 censored data, built on a new cost decomposition for base-stock policies and a biased sample-average approximation (SAA) approach. The cost decomposition allows us to propose a new coverage condition under which censored observations are informative enough for sample-efficient policy learning. Guided by this coverage condition, we design two biased SAA algorithms: an upper-biased one that achieves near-optimal sample complexity under the offline coverage condition, and a lower-biased one that actively generates the required coverage and achieves near-optimal regret online. More broadly, this biased SAA approach provides a general principle for implementing pessimism and optimism under censored feedback, which may be of independent interest.
Learning how to replenish inventory and estimating the cost of doing so require different information. We establish information-theoretic lower bounds for offline multistage inventory control with independent bounded demand. For fixed additive error in total expected cost, policy learning requires cubically many scalar...
Han-Zhang Qin, D. Simchi-Levi, Rui-Hao Zhu· 0 citations
We study online newsvendor learning with bounded demand, censored observations, and convex nonlinear shortage costs. Because stockouts can conceal the shortage magnitudes needed by gradient-based algorithms, we use randomized ordering to construct unbiased subgradient estimates. We characterize admissible randomization...
We investigate a newsvendor problem where a decision-maker observes a z-dimensional vector of covariates, and wants to learn how much stock to order given a sample of size n of censored demand data to minimize costs. Demand is censored when insufficient stock is available to meet it, and past stocking quantities are kn...
Joost F. van der Haar, Ilias Willems, J. Arts et al.· 0 citations
Retail replenishment couples a forecasting problem with a constrained stochastic allocation problem, yet forecasters are still trained to minimise statistical error rather than the cost of the decisions they induce. The mismatch is aggravated by three features of real multi-channel retailing: channels of one region dra...
In periodic-review stochastic inventory control, replenishment policies must balance holding and backlogging costs while responding to uncertain demands and evolving forecasts that may be arbitrarily correlated and nonstationary. In this general information setting, Levi, P{\'a}l, Roundy, and Shmoys (Mathematics of Ope...
Threshold-UCB is developed, which improves the expected regret to $\widetilde{\mathcal{O}}(T^{2/3})$ and reuses sales observations across inventory levels through shared estimates of demand-tail probabilities, allowing the same data to support revenue upper bounds for multiple inventories rather than a single inventory...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
