Abstract
Over the past several decades, linear programming (LP) has established itself as one of the most fundamental tools for planning. Its applications have become routine in several disciplines including those within engineering, business, economics, environmental studies and many others. One may attribute this wide spread acceptance to: (a) an understanding of the power and scope of LP among practitioners, (b) good algorithms, and (c) widely available and reliable software. Furthermore, research on specialized problems (e.g. assignment, transportation, networks etc.) has made LP methodology indispensible to numerous industries including transportation, energy, manufacturing and telecommunications, to name a few. Notwithstanding its success, we note that traditional LP models are deterministic models. That is, all objective function and constraint coefficients are assumed to be known with precision. The assumption that all model parameters are known with certainty serves to limit the usefulness of the approach when planning under uncertainty.
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Higle, J.L., Sen, S. (1996). Two Stage Stochastic Linear Programs. In: Stochastic Decomposition. Nonconvex Optimization and Its Applications, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4115-8_1
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DOI: https://doi.org/10.1007/978-1-4615-4115-8_1
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