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A reinforcement learning-enhanced multi-objective iterated greedy algorithm for weeding-robot operation scheduling problems
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AI
DOI：10.1016/j.eswa.2024.125760.png)
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Abstract
En 中文
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-weedingrobots 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.
Keywords:
Task allocation
Operation scheduling
Multi-objective optimization
Iterated greedy algorithm
Smart farms
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W