Static Task Allocation Method for Multi-Machines in Cooperative Operations Combining OGFR-GA and MLW-Prim

Yuezhong Wu, Ya Wen, Yingbo Wu, Yungang Li, Xiangming Zheng and Lingjiao Chen ()
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Yuezhong Wu: College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China
Ya Wen: College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China
Yingbo Wu: Hubei Central China Technology Development of Electric Power Co., Ltd., Wuhan 430079, China
Yungang Li: Changsha Zhongfang Robot Technology Co., Ltd., Changsha 410008, China
Xiangming Zheng: College of Urban and Environmental Sciences, Hunan University of Technology, Zhuzhou 412007, China
Lingjiao Chen: College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, China

Sustainability, 2024, vol. 16, issue 14, 1-18

Abstract: Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim’s algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency.

Keywords: agricultural machinery; genetic algorithm; optimal gene fragment; multi-loop (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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