Abstract
Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.
Highlights
The competition in the logistics industry has become increasingly fierce with the development of global e-commerce
It can be seen that the model and algorithms proposed in this study reduced the makespan while reducing the number of Automated guided vehicle (AGV), indicating that the operational efficiency of the AGV is significantly improved
Comparative numerical experiments were carried out, and the near-optimum schedules of the multi-objective function were successfully obtained. These schedules make it clear with regard to which order is sorted by which specific AGV at what speed
Summary
The competition in the logistics industry has become increasingly fierce with the development of global e-commerce. Multi-objective AGV scheduling in an automatic sorting system by using improved adaptive genetic algorithms
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