An intelligent automated guided vehicle scheduling framework for manufacturing: Balancing energy, efficiency, and task completion

In recent years, the widespread usage of Automated Guided Vehicles (AGVs) has become prevalent in material transportation systems of industries. The AGVs are known for their operational flexibility and efficiency, but efficient scheduling remains a crucial issue due to the conflicting factors, including deviation penalties for task execution times, power consumption, overall task completion time, collision risk, and utilization efficiency. To address this, this research employs a multi-objective mixed-integer programming model (MO-MIP) to formulate the scheduling problem of AGVs. The optimization algorithms, such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Reference Point-based Non-dominated Sorting Genetic Algorithm (NSGA-III) are utilized to obtain the Pareto optimal solutions in solving the scheduling problem. The simulation experiment on three distinct manufacturing workshop scenarios was conducted to examine the effectiveness of the model. The outcomes illustrated that the NSGA-II and NSGA-III exhibit reduced penalty cost, power consumption, collision risk, task completion time, and higher utilization efficiency. These algorithms also showed better computational efficiency and outperformed baseline algorithms under three manufacturing scenarios. These outcomes indicate that the proposed method is a promising solution for the industrial sector to perform material transportation in an efficient manner.