The solution of multidimensional sequencing problems encountered in the planning of capacity expansion in large-scale water resources systems by an especially efficient dynamic programming algorithm is presented. The dynamic programming algorithm results couples on the optimality of permutation schedules with the imbedded state space concept in order to effect a drastic reduction of dimensionality making it possible to efficiently solve problems of the dimension encountered in real-world water resource systems. Computational experience with the imbedded state space dynamic programming algorithm on the solution of a number of two- and three-dimensional sequencing problems from several real-world water resources systems is reported. Furthermore, the results of a sensitivity analysis on these data are also presented.