The solution of the multiperiod MILP model for long-range planning of process networks by Sahinidis et al. (Comput. Chem. Eng. 1989, 13, 1049) is addressed in this paper. The model determines the optimal selection and expansion of processes over a long-range planning horizon, incorporating multiple scenarios for varying forecasts for demands and prices of chemicals. A rigorous bilevel decomposition algorithm is proposed to reduce the computational cost in the multiperiod MILP model. The decomposition algorithm solves a master problem in the reduced space of binary variables to determine a selection of processes and an upper bound to the net present value. A planning model is then solved for the selected processes to determine the expansion policy and a lower bound to the objective function. Numerical examples are presented to illustrate the performance of the algorithm and to compare it with a full-space branch and bound method.