A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems - Miao, Zhonghua; Guo, Hengwei; Pan, Quan-ke; Peng, Chen; Xu, Ziyu (Expert systems with applications, 2025)
•We propose a population-based iterated greedy algorithm enhanced with Q-learning for a multi-weeding-robots operation scheduling problem.•An problem-related IBH is designed to generate a set of initial solutions and a local search based on the high-load robot and the critical robot is proposed.•An effective destruction phase is developed, based on the adaptive destruction intensity and Q-learning framework.•Experimental results demonstrate the effectiveness of the Q_DPIG based on the test datasets and a real case study from a farmland management center. With technological advancements, robots have been widely used in various fields and play a vital role in the production execution system of a smart farm. However, the operation scheduling problem of robots within production execution systems has not received much attention so far. To enable efficient management, this paper develops a multi-objective mathematical model concerning both the efficiency and economic indicators. We propose a population-based iterated greedy algorithm enhanced with Q-learning (Q_DPIG) for a multi-weeding-robots operation scheduling problem. An index-based heuristic (IBH) is designed to generate a diverse set of initial solutions, while an adaptive destruction phase, guided by the Q-learning framework, ensures effective neighborhood search and solution optimization. Additionally, a local search method focusing on the high-load and the critical robots is employed to further optimize the two objectives. Finally, Q_DPIG is demonstrated to be effective and significantly outperform the state-of-the-art algorithms through comprehensive test datasets and a real case study from a farmland management center.