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.

翻译：灵活性设计问题是各行业战略决策中出现的一类问题,其目标在于设计一个具有灵活性和适应性的(例如,美元,制造业)网络。潜在的组合性质和随机性目标使得灵活性设计对标准优化方法具有挑战性。在本文件中,我们为灵活性设计问题制定了强化学习框架。具体地说,我们仔细设计了探索和减少差异的机制,以确保经验成功,并显示出RL在快速适应方面的独特优势。经验性结果显示,基于RL的方法始终比传统的超自然学找到更好的解决方案。