Dynamic immune cooperative scheduling of agricultural machineries

Abstract

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

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

“…With the advancement of research, various studies have delved into the dynamic multi-region scheduling problem of agricultural machinery. Some works have focused on non-dominated neighborhood selection and taboo search immunity algorithms 9 , while others have explored the challenges of multiregion emergency scheduling, taking into account factors such as scheduling cost and time due to the time-sensitive nature of agricultural operations [10][11][12] . Additionally, there have been investigations into emergency scheduling models and algorithms for multi-region farm machinery operations, aiming to minimize scheduling costs and losses 13 .…”

Research on Agricultural Machine Scheduling in Hilly Areas Based on Improved Non-Dominated Sorting Genetic Algorithm-III

“…Agriculture in the United States is a highly mechanized, large-scale, automated, specialized and precise industry. [17] In recent years, various intelligent algorithms, such as particle swarm algorithms [18][19][20], simulated annealing algorithms [21], ant colony algorithms [22], forbidden search algorithms [23], and genetic algorithms [24][25][26], have been widely used to solve the problem of scheduling of agricultural machines. Advanced algorithms and scheduling decisions can improve the resource utilization rate and operational efficiency of agricultural machine to meet the farmers demand for agricultural machines, and reduce the scheduling time as well as the operational cost to a certain extent at the same time.…”