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Disassembly planning is aimed to perform the optimal disassembly sequence given a used or obsolete product in terms of cost and environmental impact. However, the actual disassembly process of products can experience great uncertainty due to a variety of unpredictable factors. To deal with such uncertainty, this work presents some chance constrained programming models for disassembly cost from the perspective of stochastic planning. Moreover, two hybrid intelligent algorithms, namely, one integrating stochastic simulation and neural network (NN), and another integrating stochastic simulation, genetic algorithm (GA) and neural network (NN), are proposed to solve the proposed models, respectively. Some numerical examples are given to illustrate the proposed models and the effectiveness of proposed algorithms.