General disjunctive programming, GDP, is an alternative approach to represent the formulation of traditional Mixed-Integer Nonlinear Programming, solving discrete/continuous optimization problems. By using algebraic constraints, disjunctions and logic propositions, Boolean and continuous variables are involved in the GDP formulation. The formulation process of GDP problem are more intuitive, and the underlying logic structure of the problem can be kept, so that solution can be found more efficiently. GDP has been successfully applied to Process Design and Planning and Scheduling areas. However, funtions in GDP problem sometimes could be nonconvex. Due to nonconvexites, conventional MINLP algorithms are often trapped in suboptimal solutions. Thus, solutions to nonconvex GDP has been receiving increasing attention.

Last modified

• 11/30/2018

Creator

• Kaiwen Li

DOI

• https://doi.org/10.21985/N2QX8C

Keyword

• Generalized disjunctive programming (GDP)
• Nonconvex

Rights statement

• In Copyright