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- W2220578889 abstract "Abstract : Mixed-Integer Programming has traditionally been restricted to deterministic models. Recent research has opened the door to stochastic optimization models, which are typically dynamic in nature. This project lays the foundation for stochastic dynamic mixed-integer and linear programming (SD-MIP). This project has produced several new ideas in connection with a) convexification of two-stage mixed-integer sets and b) multi-stage (including two-stage) stochastic linear programming. Together a) and b) provide the foundations for SD-MIP problems. From new concepts and algorithms to applications and software, this project has made significant breakthroughs in all aspects. This report provides a synopsis of both theoretical and computational results. As a preview, we mention that currently available deterministic MIP solvers, as powerful as they are known to be, are unable to solve SD-MIP models of modest size within an hour of computing. In contrast, our decomposition approach provides provably optimal solutions within the hour time-limit." @default.
- W2220578889 created "2016-06-24" @default.
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- W2220578889 date "2015-05-05" @default.
- W2220578889 modified "2023-09-27" @default.
- W2220578889 title "Stochastic Dynamic Mixed-Integer Programming (SD-MIP)" @default.
- W2220578889 hasPublicationYear "2015" @default.
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