Reinforcement Learning for Flexibility Design Problems

2021/01/02 by Yehua Wei, Lei Zhang, Wei, Yehua +9
Business, Management and Accounting · #Product Development and Customization #Sustainable Supply Chain Management #Supply Chain and Inventory Management

paper · pdf · doi:10.48550/arxiv.2101.00355

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.

Citations

• Dueling Network Architectures for Deep Reinforcement Learning

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