Abstract
In today’s intelligent warehouses, automated guided vehicles (AGVs) are widely used, and their scheduling efficiency is crucial to the overall performance of warehouse business. However, AGV scheduling is a complex problem, especially when there are a large number of tasks to be undertaken by multiple AGVs in a large warehouse. In this paper, we present a problem of scheduling multiple AGVs for order picking in intelligent warehouse, the aim of which is to minimize the latest completion time of all orders. After testing a variety of algorithms, we propose a hybrid water wave optimization (WWO) and tabu search (TS) algorithm for efficiently solving the problem. We test the algorithm on a set of problem instances with different sizes, and the results show that the proposed algorithm exhibits significant performance advantages over a number of popular intelligent optimization algorithms.
Supported by National Natural Science Foundation of China (Grant 61872123) and Natural Science Foundation of Zhejiang Province (Grant LR20F030002).
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Wu, X., Zhang, MX., Zheng, YJ. (2021). An Intelligent Algorithm for AGV Scheduling in Intelligent Warehouses. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_15
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