Performance comparison of reinforcement learning and metaheuristics for factory layout planning

Abstract

Factory layout planning is a time-consuming process that has a large impact on the operational performance of a future factory. Besides, changing technologies and market requirements result in a frequent reconfiguration of the factory layout. Automated planning approaches can generate high-quality layout solutions and reduce the planning time compared to mere manual planning. Recent studies indicate that reinforcement learning is a suitable approach to support the early phase of the layout planning process. In this context, reinforcement learning shows potential performance-related advantages by learning the problem-related interdependencies compared to current metaheuristic approaches, which are commonly applied to the regarded problem. However, recent studies only consider a low number of reinforcement learning approaches and regarded application scenarios. In consequence, the performance in different problem sizes and of various existing reinforcement learning approaches has not been investigated. Besides, no comparison between reinforcement learning approaches and existing metaheuristics was performed for factory layout planning. As a consequence, the potential of reinforcement learning based factory layout panning can not be evaluated appropriately. Therefore, an encompassing comparison to metaheuristics is still an open research question. Regarding this background, the performance of 13 different reinforcement learning and 7 commonly used metaheuristics for three layout planning problems with different sizes is investigated in this paper. The approaches are applied to all three layout planning problems in order to compare their performance capabilities. The results indicate that the best-performing reinforcement learning approach is able to find similar or superior solutions compared to the best-performing metaheuristics.