Knowledge Workers Scheduling Based On Stochastic Ability Promotion

Nowdays, knowledge has become one of the important factors of production of the component; knowledge workers as the carrier of knowledge become the core resources of the knowledge-based enterprise. How to arrange employees scientificly and reasonablely, so as to reduce the cost and improve the core competitiveness of the enterprise is a real problem to be solved urgently in today’s increasingly vehement competition.Knowledge workers usually have strong autonomy, multi-skilled, strong and continuous learning ability, and vulnerable to external environment, physical condition and other factors. We emphasize the characteristics that knowledge workers are vulnerable to external environment, physical condition and other factors, their ability of ascension is uncertain. On that basis, we investigate knowledge workers assignment based on stochastic ability promotion.Firstly, assume that the working status of knowledge workers is uncertain, and is subject to a probability distribution. Aiming at minimizing the total scheduling cost and maximizing the growth of knowledge workers’ skill scores, this paper develops a multi-stage scheduling model of multi-skilled knowledge workers in view of stochastic working status on the basis of Markov random sequential theory. After that, we further extend the randomness. The development of knowledge workers’ ability is uncertain nomatter in what working status. In the same way, a staff assignment model is built based on stochastic competence development.Secondly, Tasks allocation and knowledge workers scheduling is known as an NP-hard problem. Genetic Algorithm is being used to solve the problem. Be aimed at the problems that double counting and optimal solution may be lost, the rechecking and elite solution retention strategy are joined in iterative process. Finally, an example was simulated to seek the optimal scheduling policies of the model. The results show the improved Genetic Algorithm is a scientific and effective method to solve tasks allocation and scheduling of knowledge workers.