Mixed-integer linear fractional programming (MILFP)
Author: Xiang Zhao (SysEn 6800 Fall 2020)
Steward: Allen Yang, Fengqi You
Introduction
The mixed-integer linear fractional programming (MILFP) is a kind of mixed-integer nonlinear programming (MINLP) that is widely applied in chemical engineering, environmental engineering, and their hybrid field ranging from cyclic-scheduling problems to the life cycle optimization (LCO). Specifically, the objective function of the MINFP is shown as a ratio of two linear functions formed by various continuous variables and discrete variables. However, the pseudo-convexity and the combinatorial nature of the fractional objective function can cause computational challenges to the general-purpose global optimizers, such as BARON, to solve this MILFP problem. In this regard, we introduce the basic knowledge and solution steps of three algorithms, namely the Parametric Algorithm, Reformulation-Linearization method, and Branch-and-Bound with Charnes-Cooper Transformation Method, to efficiently and effectively tackle this computational challenge.
Standard Form and Properties
Consider such standard form of the MILFP:
The properties of the objective function are shown as follows:
- Numbered list item is (strictly) pseudoconcave and pseudoconvex over its domain.
- Numbered list item The local optimal of is the same as its global optimal.
Notably, several nonlinear solvers that can deal with the pseudoconvexity, such as the spatial branch-and-bound (SBB), are capable of solving the MILFP. However, the memory usage of these solvers is enormous when solving a large-scale problem that is applied in industrial scheduling or supply chain optimization project. Hence, we introduce the parametric algorithm, and reformulation-linearization method, which can reformulate the MILFP into the mixed-integer linear programming (MILP) problem, to reduce the memory usage and enhance solution efficiency.