Dynamic Multi-row Facility Layout Optimization Based on IPSO

To address the inadequate consideration of dynamic factors and unstable optimization results in manufacturing facility layout, this study develops an optimization model and algorithmic framework for dynamic multi-row facility layout problems. Fuzzy-stochastic material flows are generated by introducing normally distributed perturbations to capture the uncertainty of material demand. Based on this, a multi-period mixed-integer programming model is formulated considering material handling cost, facility relocation cost, and space utilization cost. An improved particle swarm optimization (PSO) algorithm is proposed, where chaos-based initialization is introduced to enhance search diversity. In addition, inertia weight and learning factors are dynamically adjusted to balance global exploration and local exploitation, while an improved neighborhood update mechanism based on layout structure is developed to improve convergence accuracy. Simulation results show that the improved PSO achieves higher stability and faster convergence, reducing the average number of iterations by about 30%. A case study confirms the feasibility and effectiveness of the proposed model and algorithm, reducing total cost by approximately 10.8% compared with static layouts. The proposed method demonstrates better global search capability and computational efficiency than existing metaheuristic algorithms, providing an effective approach for dynamic facility layout optimization in manufacturing systems.