Abstract
Facilities Layout Problems (FLPs) aim to efficiently allocate facilities within a given space, considering various constraints such as minimizing transportation distances. These problems are commonly encountered in various types of advanced manufacturing systems, including Reconfigurable Manufacturing Systems (RMSs). RMSs enable easier layout changes to accommodate shifts in product mix, production volume, or process requirements thanks to their modularity and changeability. Reinforcement Learning (RL) has proven its efficiency in addressing decision-making problems. Therefore, this paper introduces a comparative study between two RL algorithms to solve FLPs: Advantage Actor-Critic (A2C) and Q-learning algorithms.
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