Reinforcement Learning for Flexibility Design Problems

Yehua Wei, Lei Zhang, Ruiyi Zhang, Shijing Si, Hao Zhang, Lawrence Carin

Keywords: reinforcement learning, flexibility design, policy gradient, combinatorial optimization

Abstract: 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.

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One-sentence Summary: proposing a reinforcement learning approach for solving flexibility design problems applicable in business problems.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2101.00355/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=LtHtg4OFo1