Fast optimization algorithms for large-scale mixed-integer linear fractional programming problems

Abstract

We present three tailored algorithms for solving large-scale mixed-integer linear fractional programming (MILFP) problems. The first one combines Branch-and-Bound method with Charnes-Cooper transformation. The other two tailored MILFP solution methods are the parametric algorithm and the reformulation-linearization algorithm. Extensive computational studies are performed to demonstrate the efficiency of these algorithms and to compare them with some general-purpose mixed-integer nonlinear programming methods. A performance profile is given based on the algorithm performance analysis and benchmarking methods. The applications of these algorithms are further illustrated through an application on water supply chain optimization for shale gas production. Computational results show that the parametric algorithm and the reformulation-linearization algorithm have the highest efficiency among all the tested solution methods.