Aiming at the path planning problem of Automated Guided Vehicle (AGV) in intelligent storage, an improved Dijkstra algorithm that combines eight-angle search method and Dijkstra algorithm for path optimization is proposed. The grid method is used to model the storage environment, and the improved Dijkstra algorithm is used to optimize the route of the AGV. The simulation test of the AGV path planning process with Matlab shows that the AGV can effectively avoid obstacles by using the traditional Dijkstra algorithm and the improved Dijkstra algorithm, and then search for a collision-free optimized path from the start point to the end point; and the traditional Dijkstra algorithm In comparison, the path length planned by the improved Dijkstra algorithm is shorter and the turning angle is less, indicating that the improved algorithm is correct, feasible and effective, and has a strong global search ability.
The location selection of logistics distribution centers is a crucial issue in the modern urban logistics system. In order to achieve a more reasonable solution, an effective optimization algorithm is indispensable. In this paper, a new hybrid optimization algorithm named cuckoo search-differential evolution (CSDE) is proposed for logistics distribution center location problem. Differential evolution (DE) is incorporated into cuckoo search (CS) to improve the local searching ability of the algorithm. The CSDE evolves with a coevolutionary mechanism, which combines the Lévy flight of CS with the mutation operation of DE to generate solutions. In addition, the mutation operation of DE is modified dynamically. The mutation operation of DE varies under different searching stages. The proposed CSDE algorithm is tested on 10 benchmarking functions and applied in solving a logistics distribution center location problem. The performance of the CSDE is compared with several metaheuristic algorithms via the best solution, mean solution, and convergence speed. Experimental results show that CSDE performs better than or equal to CS, ICS, and some other metaheuristic algorithms, which reveals that the proposed CSDE is an effective and competitive algorithm for solving the logistics distribution center location problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.