United States
http://https://congshi-research.github.io/
University of Miami - Department of Management
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Social Media; Key Opinion Leaders; Advertising Campaigns; Multi-Nomial Logit Model; Selection; Scheduling
learning, pricing, reusable resources, service systems, multi-armed bandit, coupling analysis
offline learning, feature-based pricing, demand censoring, causal inference, regret analysis
process flexibility, flexible production, multi-period, capacity planning, chaining condition
inventory, fixed costs, censored demand, nonparametric, learning algorithms, regret analysis
joint pricing and inventory control, strategic customers, optimal policy, mechanism design
algorithm, joint pricing and inventory control, lost-sales, censored demand, nonparametric
revenue management, network, dynamic pricing, nonparametric, online learning, stochastic gradient descent, asymptotic optimality
inventory, lost-sales, lead time, base-stock policy, censored demand, nonparametric, learning algorithms, regret analysis
inventory, dual-sourcing, censored demand, nonparametric, learning, regret analysis
online learning, assortment planning, Markov chain choice model, capacity, regret analysis
on-demand grocery or food delivery, demand uncertainty, Susceptible-Infected-Recovered (SIR) model, auto-regressive-moving-average (ARMA), stochastic integer programming
e-commerce, consider-then-choose, consideration set, assortment planning
online learning, pricing, reference price effect, multiple products, revenue management, multi-armed bandit
inventory; service level; ready rate; fill rate; remanufacturing; approximation algorithms
sequential competition, dynamic pricing, demand learning, Nash equilibrium, regret analysis
social media influencer, content strategy, Markov decision process, matrix completion, optimal policy, streaming video
advance scheduling; online resource allocation; online algorithms; competitive analysis
inventory, dual sourcing, dual index policy, learning, bandits, sample average approximation
online learning; online resource allocation; contextual bandit; regret analysis; advance scheduling; personalized healthcare services
inventory, random capacity, online learning algorithms, regret analysis
capacity planning; joint venture; revenue sharing; game theory; efficiency; coordination
online learning; pricing; bandits; network revenue management; queueing network; mixing times
inventory, perishable products, base-stock policy, censored demand, learning algorithms
online learning, contextual bandit, regret analysis, readmission, data-driven admission control
inventory control, multiproduct, censored demand, learning algorithms, online mirror descent
online learning algorithms, regret analysis, contextual multi-armed bandit, stochastic sub-gradient descent, online convex optimization, personalized medicine, medical decision-making
multiproduct, ordering, allocation, general upgrading, online learning, censored demand
multi-armed bandit, unbounded reward, sub-exponential reward, upper confidence bound
inventory control, data-driven, dynamic programming, statistical models, convergence rate, demand CDF
production planning, stochastic programming, mixed integer linear programming, joint service-level constraint, sample average approximation
inventory control, random yield, linear inflation rule, online learning, regret analysis
bundle pricing, online learning, loss network, reusable resources, regret analysis
