Optimization Of Task Allocation And Knowledge Workers Scheduling Based On Improved Ant Colony Algorithm

In the face of quick-response, agile, lean, and personalized mode of production, tasks allocation and workers scheduling is a strategic focus in enterprises. Knowledge workers have already gradually replaced traditional labor to be core staff in many organizations, and have already replaced machines and capital to be the most critical success factor of organization. Scientific tasks allocation and knowledge workers scheduling is an important part of rational human resources management in enterprises. Tasks allocation and knowledge workers scheduling are known as an NP-hard problem. The research problem of tasks allocation and knowledge workers scheduling is described in this paper. And the research assumption is given clearly. A mathematical model of the research problem is constructed. On the basic of this, an improved ant colony algorithm for tasks allocation and scheduling of knowledge workers was proposed. By linearly changing the number of ant and self-adaptively changing related parameters in ant route choice behavior, the algorithm can reduce the number of optimization iterations and computing time, in the same time, can avoid itself into a local optimal solution. In iterative process, elite solution retention strategy and two-chromosome encoding scheme are used. Finally, an example was repeatedly simulated with different parameters. The results showed that the improved self-adaptive ant colony algorithm is a scientific and effective method to solve tasks allocation and scheduling of knowledge workers.