Assignment Problem In Uncertain Environments And Its Hybrid Intelligent Algorithm

In actual life, the assignment problem is very universal. Traditional assignment problem, that is, the assignment problem in certain environments has been studied very well. And we have many ways to solve such kind of problem.However we often meet with uncertain phenomena when we make some decisions. In more detail, as for the assignment problem, before the tasks are finished, the cost, the time and the profits of any kind of assignment plan are all uncertain. Therefore, if we want to get a directive decision, we can statistically or roughly estimate the data in efficiency matrix. As a result, the assignment problem in uncertain environments is obtained. And it is true that such kind of problem is closer to the real life.In our thesis, two kinds of models that include random parameters and fuzzy parameters respectively are firstly constructed. Secondly, enlightened by Liu's thought that solve the uncertain programming with hybrid intelligent algorithm, we design a hybrid intelligent algorithm, that is, tabu search based on simulation to achieve the approximate best solution of the six above-mentioned models. Lastly, two specific examples are listed to verify the efficiency of our algorithm.Now we shall list our contribution to the assignment problem:1. Expected value goal programming model, chance-constrained goal programming model and dependent-chance goal programming model of random assignment problem are constructed.2. Expected value goal programming model, chance-constrained goal programming model and dependent-chance goal programming model of fuzzy assignment problem are constructed.3. A kind of hybrid intelligent algorithm is designed to solve the above-mentioned models.