Parametric solution algorithms for large-scale mixed-integer fractional programming problems and applications in process systems engineering

In this work, we proposed novel parametric algorithms for solving large-scale mixed- integer linear and nonlinear fractional programming problems. By developing an equivalent parametric formulation of the general mixed-integer fractional program (MIFP), we propose exact parametric algorithms based on the root-finding methods for the global optimization of MIFPs. We also propose an inexact parametric algorithm that can potentially outperform the exact parametric algorithms for some types of MIFPs. Extensive computational studies are performed to demonstrate the efficiency of these parametric algorithms and to compare them with the general-purpose mixed-integer nonlinear programming methods. The applications of the proposed algorithms are illustrated through a case study on process scheduling and demonstrate the economic benefits of applying the proposed algorithms to practical application problems.