عنوان مقاله [English]
In this paper, a metahevristic algorithm, based on the Ant ColonyOptimization (ACO) method, is presented for solving stochastic
assignment problems. In a stochastic assignment problem, it is assumed that agents will arrive with a known distribution function
and the working time of each agent is also a stochastic variable and can be determined by a normal distribution function. Furthermore, it is also assumed that the proficiency of each agent is a stochastic variable. After modeling the problem, an ACO algorithm is developed to solve the model. Furthermore, in an evaluation phase of the objective function, a simulation algorithm is also presented. Finally, the convergence of the proposed algorithm is shown on some randomly generated test problems. Computational results show the efficiency of this algorithm in comparison to the other techniques for solving stochastic assignment problems.