Bandit-Based Monte-Carlo Planning for the Single-Machine Total Weighted Tardiness Scheduling Problem

摘要

The balance of exploration and exploitation is the essence of any successful meta-heuristic. The Multi-armed Bandit Problem represents a simple form of this general dilemma. This paper describes two heuristic optimization methods that use a simple yet efficient allocation strategy for the bandit problem called UCB1 to control the optimization process.The algorithms are applied to the well known Single Machine Total Weighted Tardiness Problem and the results compared to the results of other successful meta-heuristics for this scheduling problem.