Research on the solution models and methods for random assignment problems based on synthesis effect and genetic algorithm

In this paper, we systematically discuss the assignment problem whose efficiency are random variables. Firstly, by using the restriction and complementary relation between mathematical expectation and variance in decision making and the synthesis effect description of random variable, we propose a solution model for random assignment problem. Further, by combining the characteristic of assignment problem, we give the concrete scheme based on genetic algorithm. Finally, we consider its convergence by using Markov chain theory, and analyze its performance through an example. All these indicate that, this solution model can effectively merge decision preferences into the assignment process, it possess many features of strong interpretability, easy operation and higher computation efficiency, so it can be widely used in many fields such as manufacturing and management, optimization scheduling etc.