Remanufacturing, which processes end-of-life products with disassembly, testing, reprocessing, and reassembly operations in such a way that their quality and performance is restored, has attracted substantial interest in recent years. In this article, we address a capacitated dynamic lot-sizing problem with remanufacturing, in which there are heterogeneous demand streams for newly manufactured products assembled wholly by brand-new components and remanufactured ones reassembled by reprocessed components. The demand for remanufactured products could also be satisfied by new ones, as remanufactured products do not meet self-demand, but not vice versa. For this planning problem, a fuzzy mixed integer linear programming model, which considers the uncertainties of market demands, the quantity of end-of-life products available, the remanufacturable rate, selling prices, unit cost values, and capacity constraints, is developed by triangular fuzzy numbers. After that, the fuzzy mixed integer linear programming model is transformed into a crisp equivalent by clarifying fuzzy constraints and objective. The solution approach is designed by genetic algorithm, in which self-adaptive formula is adopted to resolve difficulty in obtaining optimum value of crossover probability and mutation probability. Finally, a numerical example is suggested to demonstrate the applicability and effectiveness of the proposed model and solution approach.