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首页> 外文期刊>PLoS One >Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm
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Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

机译：通过使用两个自适应遗传算法和多自适应遗传算法在无人（智能）仓库自动分拣系统中的多目标AGV调度

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摘要

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.
机译：自动导向车辆（AGV）是一种具有高安全性和优异可用性的物流运输车辆，可真正实现无人驾驶运行。 AGV在智能仓库或无人机仓库中使用的用于分类可以提高仓库的效率，提高企业的竞争力。 在本文中，开发了一种多目标数学模型，并与两个自适应遗传算法（AGA）和多自适应遗传算法（Maga）集成，以通过采取充电任务和可变速度来优化AGV的任务调度 AGV考虑到最小化MakEspan，所使用的AGV数量和电力消耗量。 数值实验表明，Maga是三种算法中最好的。 优化前后的目标价值约为30％，这证明了模型和Maga的合理性和有效性。

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