An efficient global optimization algorithm for mixed-integer nonlinear fractional programs with separable concave terms

Abstract

It could be a very challenging task to globally optimize large-scale mixed-integer fractional programs (MIFP) with separable concave and fractional terms in the objective function. To address this computational challenge, we propose a novel and efficient global optimization algorithm, which integrates an inexact parametric algorithm based on Newton's method and a successive piecewise linear approximation algorithm. To demonstrate the efficiency of this algorithm, we use it to optimize the economic and environmental performance of a manufacturing process for biodiesel and bioproducts from microalgae. The problem is solved with several global optimization methods. Computational results show that the proposed global optimization algorithm is more efficient than general-purpose MINLP solvers when solving the special type of MIFP problems.