Random fuzzy production and distribution plan of agricultural products and its PSO algorithm

We present a random fuzzy tactical planning model for the production and distribution of agricultural products because of the uncertainties encountered in the whole process. In the proposed model, the crops' yields are uncertain and assumed to be characterized by random fuzzy variables with known probability and possibility distributions. The model approach selected is a two-stage hybrid uncertain programming in which the decisions in a first stage are designed to meet the uncertain outcomes in a second stage. Since it is always difficult to handle with the random fuzzy model directly, we apply an approximation approach (AA) to evaluate the value of the objective function and discuss the convergence the AA. Considering that the approximating model is neither linear nor convex, it cann't be solved via the conventional optimization algorithm. Therefore, an approximate-based hybrid particle swarm optimization (PSO) algorithm is designed to solve the proposed model. Finally, an application example is presented to highlight the significance of the random fuzzy production and distribution model as well as the effectiveness of the solution method.