Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network

Li, Wei

doi:10.1155/2022/1231642

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The complex geographical, multifactorial, and dynamic nature of multi-region farm machinery scheduling in these areas often poses challenges for traditional scheduling schemes to operate optimally due to the unique geography, thus hindering the development of agricultural mechanization [2][3][4][5] . Addressing the need to efficiently complete all farmland tasks simultaneously while minimizing scheduling time and costs has become a pressing issue in agricultural machinery scheduling in hilly mountainous regions [6][7][8] . This paper aims to delve into the multi-region agricultural machinery scheduling problem in these areas by developing mathematical models and utilizing advanced algorithms to optimize routes and timing for each agricultural machine, ultimately maximizing resource utilization.…”

Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

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Optimizing the scheduling of farm machinery is essential to meet farmers' requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across various regions, aiming to minimize scheduling costs and reduce scheduling time. Initially, a multi-constraint hybrid clustering algorithm is employed to assign farmland to each farm machinery cooperative by clustering before scheduling. Subsequently, an enhanced version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is proposed, integrating a local search strategy based on congestion-based neighborhood search and the Simulated Annealing (SA) algorithm to develop the NSGA-II-SA algorithm. This hybrid multi-objective evolutionary algorithm effectively optimizes scheduling costs and time. The model's validity and the algorithm's superiority are demonstrated through a Web-based multi-region agricultural machine scheduling system and an example study. Experimental results show that the NSGA-II-SA algorithm significantly reduces scheduling costs and time, as well as the number of dispatched farm machines, outperforming other algorithms with reductions of 9.8%, 3.1%, and 8.7% in total scheduling costs, and 12.5%, 13.4%, and 11.6% in total scheduling time. This research establishes a theoretical framework for multi-region agricultural machine scheduling in hilly and mountainous areas, enhancing agricultural production efficiency.

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Section: Introductionmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

View full text Add to dashboard Cite

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Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

Guo,

Zhang,

Chang

et al. 2024

Computers and Electronics in Agriculture

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Research on emergency scheduling based on improved genetic algorithm in harvester failure scenarios

Liu,

Zhang,

Zhao

et al. 2024

Front. Plant Sci.

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In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm’s crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling.

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Research on Scheduling Algorithm of Agricultural Machinery Cooperative Operation Based on Particle Swarm Neural Network

Cited by 3 publications

References 9 publications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Research on a multiobjective cooperative operation scheduling method for agricultural machinery across regions with time windows

Research on emergency scheduling based on improved genetic algorithm in harvester failure scenarios

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