Improved Integer Programming Approaches for Chance-Constrained Stochastic Programming
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2938-2943.
Abstract
The Chance-Constrained Stochastic Programming (CCSP) is one of the models for decision making under uncertainty. In this paper, we consider the special case of the CCSP in which only the right-hand side vector is random with a discrete distribution having a finite support. The unit commitment problem is one of the applications of the special case of the CCSP. Existing methods for exactly solving the CCSP problems require an enumeration of scenarios when they model a CCSP problem using a Mixed Integer Programming (MIP). We show how to reduce the number of scenarios enumerated in the MIP model. In addition, we give another compact MIP formulation to approximately solve the CCSP problems.
@inproceedings{ijcai2013p431,
title = {Improved Integer Programming Approaches for Chance-Constrained Stochastic Programming},
author = {Yanagisawa, Hiroki and Osogami, Takayuki},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2938--2943},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/431},
}
TY - CPAPER TI - Improved Integer Programming Approaches for Chance-Constrained Stochastic Programming AU - Yanagisawa, Hiroki AU - Osogami, Takayuki T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2938 EP - 2943 UR - https://ijcai.org/proceedings/2013/431 ER -Copied
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