A Cooperative Scheduling Based on Deep Reinforcement Learning for Multi-Agricultural Machines in Emergencies

Weicheng Pan, Jia Wang () and Wenzhong Yang
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Weicheng Pan: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Jia Wang: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Wenzhong Yang: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China

Agriculture, 2024, vol. 14, issue 5, 1-16

Abstract: Effective scheduling of multiple agricultural machines in emergencies can reduce crop losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement learning for multi-agricultural machines with deadlines is designed to minimize makespan. With the asymmetric transfer paths among farmlands, the problem of agricultural machinery scheduling under emergencies is modeled as an asymmetric multiple traveling salesman problem with time windows (AMTSPTW). With the popular encoder-decoder structure, heterogeneous feature fusion attention is designed in the encoder to integrate time windows and asymmetric transfer paths for more comprehensive and better feature extraction. Meanwhile, a path segmentation mask mechanism in the decoder is proposed to divide solutions efficiently by adding virtual depots to assign work to each agricultural machinery. Experimental results show that our proposal outperforms existing modified baselines for the studied problem. Especially, the measurements of computation ratio and makespan are improved by 26.7% and 21.9% on average, respectively. The computation time of our proposed strategy has a significant improvement over these comparisons. Meanwhile, our strategy has a better generalization for larger problems.

Keywords: multi-agricultural machines scheduling; deep reinforcement learning; emergency scheduling; deadline; asymmetric transfer path (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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