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.)

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Capacity planning is an area in which engineers can affect business performance. The size of the expansions and the timing in which they take place are the most important variables in capacity planning. The Economic Investment Time (EIT) Model is a tool that enables planners to accurately determine the investment time in which profits resulting from the proposed expansion can be maximized. This thesis presents the results yielded by the EIT. Validation is performed using the utilization results yielded by the Newsboy Model. This paper explains the methodology used and the conclusions that can be drawn. / Master of Science