基于混合遗传算法的农机科研人员任务调度优化研究

Optimization Based-on Genetic Algorithms of Job-employee Scheduling in Agricultural Mechanization Research

农机科研人员任务调度问题属于NP-hard问题。它允许农机项目中的任务可以由项目团队中的多个农机科研人员完成实施,调度的目的是将任务分配给各科研人员,并对各农机科研人员的任务进行排序以使完成所有项目的时间最小化。本文采用基于禁忌搜索的混合遗传算法进行农机科研人员任务调度研究,提出了一种新颖直观的双染色体基因编码方法,以期达到农机科研人员任务调度决策的科学化,并在MATLAB7.0平台对算例进行仿真。

Job-employee scheduling problem in agricultural mechanization research has been known as NP-hard problem. The Job-employee scheduling problem allows one task to be implemented by several agricultural technicians out of a team. The problem is to assign each task to a technician and find a sequence for the tasks on the technician in order that the completion time of all tasks is minimized. A HGA based on taboo search and new two-chromosome encoding scheme is presented for scientific decision of this problem. Finally, an example is simulated in MATLAB7.0.