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A Tie-Breaking Rule for Discrete Infinite Horizon Optimization

We study discrete infinite horizon optimization problems without the common assumption of a unique optimum. A method based on solution set convergence is employed for finding optimal initial decisions by solving finite horizon problems. This method is applicable to general discrete decision models that satisfy a weak reachability condition. The algorithm, together with a stopping rule, is applied to production planning and capacity expansion, and computational results are reported.