25 citations · 33 across the 8 of their papers we have counts for
8 papers · 1 filter
Multi-stage and Multi-customer Assortment Optimization with Inventory Constraints
Elaheh Fata, Will Ma, David Simchi-Levi
We consider an assortment optimization problem where a customer chooses a single item from a sequence of sets shown to her, while limited inventories constrain the items offered to…
Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We consider un-discounted reinforcement learning (RL) in Markov decision processes (MDPs) under temporal drifts, ie, both the reward and state transition distributions are allowed…
Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-dependent Competitive Ratios
Will Ma, David Simchi-Levi
Motivated by the dynamic assortment offerings and item pricings occurring in e-commerce, we study a general problem of allocating finite inventories to heterogeneous customers arri…
Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses
Rong Jin, David Simchi-Levi, Li Wang +2
The recent rising popularity of ultra-fast delivery services on retail platforms fuels the increasing use of urban warehouses, whose proximity to customers makes fast deliveries vi…
Phase Transitions in Bandits with Switching Constraints
David Simchi-Levi, Yunzong Xu
We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given swit…
Hedging the Drift: Learning to Optimize under Non-Stationarity
Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applicatio…