Capacity Expansion under Forecast Uncertainty: Stochastic Integer Programming Approaches

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The project is aimed at the development of optimization techniques for planning capacity expansions when forecasted planning data are unreliable. Stochastic programming has emerged as an important tool for solving planning problems with data uncertainties. In capacity expansion problems, however, the integral nature of strategic decisions prevents the use of standard decomposition approaches that have been successful for stochastic linear programs. This project will develop efficient solution strategies for stochastic integer programs arising in capacity expansion applications. A key component of the project will be to identify special problem structures that can be exploited within solution strategies. The structural results will be used to design, analyze, and implement approximate and exact solution algorithms. The viability of the developed methodology will be demonstrated in important economic sectors such, as semiconductor wafer fabrication facilities and web hosting enterprises.Capacity expansion to meet anticipated demand growth is a key strategic concern in all industrial sectors. In high growth-high volatility industries, such as the IT sector, uncertainties in forecasts for costs, demands, and technology evolution, and the economies-of-scale in expansion costs make capacity expansion decisions very complex. Using stochastic integer programming concepts, this research project will develop an optimization based paradigm for aiding capacity expansion that explicitly address forecast uncertainty. If successful, the project will provide robust computational techniques to aid strategic capacity planning in a wide variety of industries. It is also anticipated that insights gained from this research will significantly advance the current state-of-the-art in solving multi-stage stochastic integer programs.