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Optimal capacity expansion planning when there are learning effects

Production and capacity expansion decisions are difficult to analyze when there is learning. Later production is less costly, and maybe more profitable, but the company must endure high initial production costs. Mixed integer programming models are presented for optimizing coordinated production and capacity expansion plans in the face of such learning effects. An illustrative model is developed, optimized, and the types of strategies it selects are discussed.