We show that, under certain conditions, instead of solving stochastic capacity expansion problems, we will obtain the same optimal solution by solving deterministic equivalent problems. Since only the first decision must be implemented immediately, knowing the optimal first decision is nearly as good as knowing the entire optimal sequences. Hence if we can solve the problem with 'big enough' finite horizon such that the first decision remains optimal for longer than this finite horizon, then we identify the 'big enough' finite horizon as forecast horizon. The forward dynamic programming recursion can be used to solve a finite horizon problem. An efficient forward algorithm has been developed to obtain the first optimal decision and forecast horizon. A heuristic algorithm also has been derived to prove an initial decision is within known error bound of the optimal first decision. Several examples are examined to investigate how a decision will be affected by randomness. (Abstract shortened with permission of author.
