AI Bibliography
AI Bibliography |
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Haneveld, W. K. K., van der Vlerk, M. H., & Romeijnders, W. (2019). Stochastic programming: Modeling decision problems under uncertainty. Springer International Publishing. |
| Resource type: Book ID no. (ISBN etc.): 9783030292195 BibTeX citation key: Haneveld2019 View all bibliographic details  |
Categories: Cognitive Science, Computer Science, Decision Theory, General, Military Science Subcategories: Decision making, JADC2, Machine intelligence Creators: Haneveld, Romeijnders, van der Vlerk Publisher: Springer International Publishing Collection: |
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| Abstract |
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This book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems.
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