Fuzzy optimization of knowledge worker scheduling based-on hybrid genetic algorithms

Knowledge worker scheduling problem in knowledge enterprise has been known as a NP-hard problem. The knowledge worker scheduling problem allows one task to be implemented on by several knowledge workers of a team. The problem is to assign each task to a knowledge worker and find a sequence for the tasks on the knowledge worker in order that the satisfaction of all customers is maximized. This paper investigated the fuzzy knowledge worker scheduling in knowledge enterprise using hybrid genetic algorithms (HGA) that is based on tabu search, proposed a novel two-chromosome-encoding method, and a case study with the MATLAB 7.0 platform is carried to test this method.