This paper studies multi objective stochastic optimization models with chance constraints. The use of random variables as input parameters in mathematical models is one of the conventional approaches to model various problems under uncertainty. Also, the chance constraints allow the decision maker to reject the corresponding constraint with the probability of up to a specified value. One of the challenges of such models is the chance constraints and these models cannot be solved directly. The given chanced constraints must first be converted to a deterministic case and then solved by applying the available techniques. One of the most important methods for converting the chance constraints into the deterministic one is to use the distribution function of the random variables that should be available to decision makers but usually there is no exact distribution function of a random variable in the real problems. For this reason, this paper proposes a sampling-based approach to convert chance constraints to deterministic ones, which meets the chance constraints with the greatest probability. The weighted sum method is used to solve the multi objective deterministic model. Finally a numerical example is presented to illustrate the performance of the proposed method. 

Keywords: Stochastic Optimization, Multi Objective Optimization, Chance Constraint Optimization, Sampling-based Method

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