Unequal area facility layout problem considering transporters interaction- a queuing theory and machine learning approach

This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.