Efficient resource allocation in material handling systems (MHSs) is vital for intelligent manufacturing cells with multi-resource collaboration. The interdependencies among diverse equipment types create complex interactions that increase analytical complexity, especially under stochastic batch transportation where batch sizes depend on buffer jobs and Automated Guided Vehicle (AGV) capacities. Traditional modeling approaches struggle to capture the complex dynamics of multi-level fork/join nodes under these conditions, leaving a gap in effective analysis methods. Here, we develop an open queueing network model with finite buffers, utilizing the Decomposition of State Space Method (DSSM) and Continuous-Time Markov Chain (CTMC) to systematically analyze each node's state. An iterative algorithm is employed to compute the system's performance metrics. We conduct numerical experiments comparing the approximate results of our model with simulation outcomes. Our results demonstrate that the proposed approach accurately and effectively captures the complex dynamics of multi-resource collaborative MHSs, addressing the limitations of traditional methods. This work provides a robust analytical tool for optimizing resource allocation in intelligent manufacturing systems, advancing the field of intelligent manufacturing.
Zhang et al. (Wed,) studied this question.