Application of Ant Colony Algorithm and Genetic Algorithm in Multi-Objective Dynamic Equalization Scheduling of AGVs - Liang, Lu; Liu, Mingyuan; Xiong, Ziyi (Transactions on Computer Science and Intelligent Systems Research, 2025)
In this paper, an optimization method based on heuristic genetic algorithm is proposed for AGV multi-objective dynamic balanced scheduling. Firstly, the warehouse environment is constructed by raster modeling method, and the AGV path planning is transformed into a node traversal situation in a two-dimensional coordinate system, and the spatial relationship of multiple elements is defined. Then, a single-objective constraint model is established to minimize the longest path AGV picking time as the objective, and the objective function is constructed by combining the Manhattan distance and task processing time. Then the ant colony algorithm is introduced to solve the path planning, and the optimization efficiency of the algorithm is improved by dynamically adjusting the parameters. Further, a multi-objective scheduling model is constructed, taking into account the three indexes of total path length, task load balance and maximum time consumption, and a genetic algorithm is used to solve the problem. The task allocation strategy is optimized through a series of operations, and the fitness function is designed to achieve multi-objective transformation. This research provides theoretical support and practical reference for the efficient scheduling of unmanned warehousing system.