A stochastic multi-product lot-sizing and sequencing problem is studied. Two kinds of uncertainties are integrated into the model: defective items due to the process imperfections and random lead times because of randomly arising machine breakdowns and uncertain repair times. There are also sequence-dependent setup times between two items of different types. The objective is to find a sequence of lots and lot sizes maximizing the probability of demand satisfaction for all products. A decomposition approach has been proposed in the literature to reduce this problem to a sequence of known optimization problems with different algorithms available for each of them. However, a proper evaluation of the practical performance of the whole method has never been presented. The goal of this paper is to analyze and compare the behavior of different solution frameworks (with and without decomposition) and techniques for the considered problem.