Optimizing milk-run system and IT-based Kanban with artificial intelligence: an empirical study on multi-lines assembly shop floor

In this research, a dynamic optimization of milk-run system (MRS) managed with an IT-based Kanban (ITK) is performed. The main tasks are to i) explore how artificial intelligence may allow MRS to choose the most efficient path and ii) measure the impact on the main production parameters. The study explores the Kanban signals activating an ant colony optimization algorithm that finds the best path from supermarket to the lines. Then, genetic algorithm solves an objective function to find the optimal delivery times according to the paths found. The optimal path is dynamically found for each MRS supply cycle. Within the empirical results, significant improvements for production parameters and overall system performance have been appraised. The lead time and material handling time show a strong decrease to 39% and 48%, respectively, while work in process decreases of 22% for all assembly lines. Workstation starvation decreased by 43% and machine saturation increased by 37%.