Research on Task Assignment Method Based on Multi-Strategy Improved Whale Migration Hybrid Genetic Algorithm

Abstract

In modern supply chain systems, task allocation is indispensable to logistics transportation. Sound and efficient allocation strategies are key to improving distribution efficiency and resource utilization. To address the limitations of traditional optimization algorithms in logistics task allocation, such as poor dynamic adaptability, susceptibility to local optima, and insufficient robustness under coupled operational constraints, this paper develops an optimization model with the objective of minimizing total cost and proposes a multi-strategy improved whale migration hybrid genetic algorithm. The algorithm enhances solution quality and convergence performance through tournament selection, improved order crossover operator, 2-opt operator, and hierarchical competition strategy. Comparative experiments with multiple algorithms are conducted to verify the effectiveness of the model and algorithm on small-, medium-, and large-scale instances. Simulation results show that the proposed model is applicable to unmanned vehicle distribution task allocation under coupled operational constraints, and the WMA-GA effectively solves logistics task allocation problems, exhibiting competitive performance in path optimization, convergence speed, and solution stability.