Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Convex Optimization
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
Management Science
Approximation algorithms for budgeted learning problems
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
A Learning Approach for Interactive Marketing to a Customer Segment
Operations Research
Optimal employee retention when inferring unknown learning curves
Proceedings of the Winter Simulation Conference
Optimal Dynamic Assortment Planning with Demand Learning
Manufacturing & Service Operations Management
| Hi-index | 0.00 |
This paper considers the multiarmed bandit problem with multiple simultaneous arm pulls and the additional restriction that we do not allow recourse to arms that were pulled at some point in the past but then discarded. This additional restriction is highly desirable from an operational perspective, and we refer to this problem as the “irrevocable multiarmed bandit” problem. We observe that natural modifications to well-known heuristics for multiarmed bandit problems that satisfy this irrevocability constraint have unsatisfactory performance and, thus motivated, introduce a new heuristic: the “packing” heuristic. We establish through numerical experiments that the packing heuristic offers excellent performance, even relative to heuristics that are not constrained to be irrevocable. We also provide a theoretical analysis that studies the “price” of irrevocability, i.e., the performance loss incurred in imposing the constraint we propose on the multiarmed bandit model. We show that this performance loss is uniformly bounded for a general class of multiarmed bandit problems and indicate its dependence on various problem parameters. Finally, we obtain a computationally fast algorithm to implement the packing heuristic; the algorithm renders the packing heuristic computationally cheaper than methods that rely on the computation of Gittins indices.