Optimization of teacher volunteer transferring problems using greedy genetic algorithms

In this paper, an evolutionary approach based on a greedy genetic algorithm (GA) is studied to serve as an efficient solver for real world teacher volunteer transferring problems (TVTPs). In the proposed approach, the transferring problems are first mathematically formulated into constrained combinational optimization problems and then, an improved neighborhood-search based on greedy search rules is embedded into the mutation operator of the proposed GA method to explore optimal solutions. For verifying the correctness and efficiency of the proposed methods, several real-world transferring cases are studied, and the results show the benefits while adopting the proposed approach in the practical application which can greatly increase the successful transferring numbers comparing to the official TVTP results.