Hybrid Particle Swarm Optimization Algorithm for Flexible Task Scheduling

A large project in a company is often divided to several subtasks, which would be assigned to different people with variant abilities to the same task. So whether the tasks are scheduled properly would determine the quality or the efficiency of team collaboration. A hybrid particle swarm optimization (PSO) algorithm is putted forward. Subtasks are disassembled from the project by using the task tree relations, and the tree structure is modeled into a task matrix. Moreover, task-time matrix is used to indicate the people abilities to complete the tasks. Then the hybrid algorithm was presented, in which simulated annealing method is added in particle swarm optimization to improve the capability of seeking the best allocating results. Finally, a simulation experiment is carried out by using the proposed algorithm, the comparing results show that the convergent velocity is fast and the optimizing ability is preferable.