Optimal schedule for agricultural machinery using an improved Immune-Tabu Search Algorithm

Zhu, Xinmeng; Ding, Yongsheng; Cai, Xin; Wang, Haishan; Zhang, Xiangfei

doi:10.23919/chicc.2017.8027793

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…After completing one plot, he goes to the next plot until all the allocated plots are completed and returns to the agricultural machinery center. At this moment, cross-regional operations need to re-plan the operation sequence of each agricultural machinery to ensure the shortest transportation path of each agricultural machinery, and thus maximize income [9].…”

Section: Path Planningmentioning

confidence: 99%

“…Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algorithm proposed by Rao et al in 2010. It is a new algorithm proposed after genetic algorithm, ant colony algorithm, and particle swarm algorithm [9,10]. A new swarm intelligence algorithm can be used to solve complex multi-objective problems [11].…”

Section: Introduction Of Teaching and Learning Optimization Algorithmmentioning

confidence: 99%

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Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

et al. 2022

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With the adjustment of agricultural industry structure and the acceleration of land transfer in our country, the appropriate management level of continuous, large scale and intensive farming has remarkably improved, which has put forward a higher requirement for the level of agricultural machinery. According to statistics, the comprehensive mechanization rate of farming, planting and harvesting of major crops in China has exceeded 70% in 2020. However, the development level of mechanization in different provinces and regions is still unbalanced, so is the demand and supply of seasonal agricultural machinery operation. Cross regional operation of agricultural machinery is still a common occurrence. So a scientific, efficient and low-cost agricultural machinery allocation scheme is to be constructed so as to solve the imbalance of development level between regions of agricultural mechanization and the contradiction between supply and demand of agricultural machinery in operation seasons and to realize the rational allocation of agricultural machinery in cross regional operation. The allocation scheme can increase the machinery owners' income by improving the utilization rate and allocation cost of agricultural machinery.This study systematically analyzes and summarizes the allocation mode of agricultural machinery in the reclamation area, constructs relevant data models and solution methods, and ultimately comes to the effective solution of the operation relationship allocation model and path optimization model under different allocation modes of agricultural machinery based on TLBO optimization theory and method, which effectively solves the problem of rational and efficient allocation of agricultural machinery in cross regional operation of agricultural machinery.

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Section: Path Planningmentioning

confidence: 99%

Section: Introduction Of Teaching and Learning Optimization Algorithmmentioning

confidence: 99%

Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

et al. 2022

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“…The total work volume of a single agricultural machine in the assigned service relationship must be completed within the total workable days D. 1 1…”

Section:  mentioning

confidence: 99%

“…At present, the allocation of agricultural machinery is mainly based on expert system evaluation and artificial decision-making. There are few automatic allocation systems and allocation tools [1]. The ant colony optimization algorithm is used to generate automated follow-up routes for agricultural machinery equipped with autonomous driving navigation systems, thereby improving work efficiency [2], which can also effectively and quickly solve the agricultural machinery allocation tasks in emergency situations [3].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on Particle Swarm Optimization Algorithm

et al. 2021

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“…In the present agricultural production process, the agricultural machinery scheduling is evaluated by expert system and controlled by artificial decision. 1 Expert system and artificial decision cannot solve the problems of scheduling efficiency and resource optimization well. Therefore, we need to design a reasonable and efficient agricultural machinery scheduling algorithm to reduce the waste of agricultural resources and improve the productivity of agricultural machinery.…”

Section: Introductionmentioning

confidence: 99%

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

Jiang

Yang

et al. 2022

Int J of Intelligent Sys

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Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune-following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution.

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Optimal schedule for agricultural machinery using an improved Immune-Tabu Search Algorithm

Cited by 8 publications

References 12 publications

Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

Research on the Optimized Management of Agricultural Machinery Allocation Path Based on Teaching and Learning Optimization Algorithm

Optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on Particle Swarm Optimization Algorithm

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

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