The expansion of product portfolios, the reduction of product life cycles and the volatility of markets pose significant challenges for production systems and their control. Concurrently, these trends present a challenge in achieving an optimal balance between logistical performance and internal company costs. The discordance between the escalating demands of customers on logistics performance, exemplified by metrics such as throughput time and schedule reliability, and the cost-driven corporate objective of minimizing work-in-process and maximizing machine utilization, is becoming increasingly challenging to reconcile. The application of reinforcement learning (RL) is a significant machine learning (ML) approach for overcoming these challenges. In comparison with other ML approaches, RL facilitates direct interaction with production systems and is consequently well suited for controlling them in operational use. Despite the extensive body of research on RL approaches for production control tasks, there is a paucity of literature addressing the influence of these approaches on key logistical targets for logistics performance and costs. The article’s added value derives from the systematic literature review it conducts, which provides researchers and practitioners with an overview of how existing RL approaches influence central logistical target variables. Furthermore, it highlights blind spots in the research landscape. The results indicate the existence of a substantial number of approaches; however, their distribution across control tasks is disproportionate. Furthermore, it is evident that there are distinct discrepancies in the classification system with respect to the impact on logistical target variables.
Schneider et al. (Thu,) studied this question.