Application of Inhomogeneous QMIX in Various Architectures to Solve Dynamic Scheduling in Manufacturing Environments

In light of the growth in data availability, the manufacturing industry is experiencing a growing change in its needs and shape, which necessitates the use of more efficient data-driven methodologies in real-time production scheduling. Conventional dynamic scheduling approaches, designed for non-dynamic problem sizes, struggle in dealing with the inherent volatility and complexity of contemporary production scheduling scenarios. As an attempt to meet the demands of near real-time decision-making mechanisms on the shop floor, this study explores variations of QMIX, including local QMIX (LQMIX), where separate instances are employed for specific tasks, and gated QMIX (GQMIX), which utilizes specific agents for tasks while employing a central mixing network. Scheduling systems may not satisfy the recent requirements, emphasizing the need for more adaptive systems. Utilizing QMIX, manufacturing operations can be streamlined by integrating the collaborative synergy of multiple agents. Reinforcement learning models are trained using QMIX and benchmarked against heuristic dispatch strategies such as Shortest Path First as one of the most popular method used in the community. The experimental findings highlight the effectiveness of QMIX, in particular the original version, in tackling the challenges of dynamic scheduling within the manufacturing domain. QMIX exhibits superior performance compared to alternative algorithms and heuristic dispatching methods in specific contexts. Nonetheless, the study underscores the imperative of striking a balance between adaptability and specialization.