Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7543639 | Operations Research for Health Care | 2016 | 6 Pages |
Abstract
We develop a constraint generation solution method for robust optimization problems in radiation therapy in which the problems include a large number of robust constraints. Each robust constraint must hold for any realization of an uncertain parameter within a given uncertainty set. Because the problems are large scale, the robust counterpart is computationally challenging to solve. To address this challenge, we explore different strategies of adding constraints in a constraint generation solution approach. We motivate and demonstrate our approach using robust intensity-modulated radiation therapy treatment planning for breast cancer. We use clinical data to compare the computational efficiency of our constraint generation strategies with that of directly solving the robust counterpart.
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Authors
Houra Mahmoudzadeh, Thomas G. Purdie, Timothy C.Y. Chan,