A Q-learning-assisted memetic algorithm for joint vehicle scheduling problem for harvesting and transportation in smart agriculture

As smart agriculture continues to advance, the integration of agricultural activities with intelligent vehicle technologies is offering significant opportunities while posing new challenges. This paper focuses on the harvesting and transportation joint vehicle scheduling problem (HTJVSP) in smart agriculture, aiming to minimize the maximum completion time. The study proposes a joint vehicle scheduling model and introduces a novel solution approach based on a Q-learning-assisted memetic algorithm (Q-MA). The Q-MA algorithm features a hybrid initialization strategy that generates a diverse and high-quality initial population. During the evolutionary phase, three tailored crossover strategies are proposed, specifically designed to align with the unique characteristics of HTJVSP. These strategies enhance the exploration of the search space and promote faster convergence. In the local search phase, Q-learning acts as an adaptive decision- making agent, dynamically selecting the most effective operator from four specialized local search methods, thereby improving solution refinement and accelerating convergence. Finally, the experimental results and ANOVA analysis confirm that the Q-MA outperforms state-of-the-art competitors from the benchmark set, demonstrating the effectiveness of the proposed algorithmic components and its superior performance in solving the HTJVSP.