Reinforcement Learning for Flexibility Design Problems
This work tackles the challenge of combinatorial and stochastic objectives in flexibility design problems for strategic decision-makers, offering an incremental improvement over existing methods.
This paper addresses flexibility design problems, which involve designing adaptive networks in strategic decision-making. The authors developed a reinforcement learning (RL) framework that consistently finds better solutions compared to classical heuristics.
Flexibility design problems are a class of problems that appear in strategic decision-making across industries, where the objective is to design a ($e.g.$, manufacturing) network that affords flexibility and adaptivity. The underlying combinatorial nature and stochastic objectives make flexibility design problems challenging for standard optimization methods. In this paper, we develop a reinforcement learning (RL) framework for flexibility design problems. Specifically, we carefully design mechanisms with noisy exploration and variance reduction to ensure empirical success and show the unique advantage of RL in terms of fast-adaptation. Empirical results show that the RL-based method consistently finds better solutions compared to classical heuristics.