Physics-Informed Dynamic Cell Layout Planning for Matrix-Structured Manufacturing Workshops Under Volatile Environment

Abstract

Abstract. Modern manufacturing enterprises face growing challenges due to frequent production disturbances and intensified demand fluctuations. As a resilient manufacturing paradigm in the era of Industry 5.0, the matrix manufacturing system (MMS) can effectively accommodate multivariety and multibatch production requirements. However, traditional layout algorithms lack effective dynamic response mechanisms and have limited capability for real-time optimization of cell configurations under a volatile environment. To address this issue, we propose a dynamic cell layout planning method based on a consensus-enhanced fruit fly optimization algorithm (CE-FOA), in which physical constraints are embedded into the optimization process through feasible layout representation, fitness evaluation, and constraint-guided search. A consensus-driven evolutionary mechanism is incorporated into the conventional FOA to enhance global search efficiency and convergence stability, while a logistics relationship dimensionality reduction strategy is devised to lower computational complexity during optimization. Case study results from an MMS-based optoelectronic-pod (OP) workshop show that CE-FOA outperforms traditional FOA and simulated annealing (SA) in solution quality and convergence rate. These results validate the effectiveness and superior performance of the proposed approach, demonstrate the practical value of embedding manufacturing physical knowledge into dynamic layout optimization, and provide a new solution for dynamic workshop layout planning under the MMS paradigm.