Date of Original Version
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version is available at http://dx.doi.org/10.1016/j.compchemeng.2009.05.014
Abstract or Description
In this paper we consider the solution methods for mixed-integer linear fractional programming (MILFP) models, which arise in cyclic process scheduling problems. We first discuss convexity properties of MILFP problems, and then investigate the capability of solving MILFP problems with MINLP methods. Dinkelbach’s algorithm is introduced as an efficient method for solving large scale MILFP problems for which its optimality and convergence properties are established. Extensive computational examples are presented to compare Dinkelbach’s algorithm with various MINLP methods. To illustrate the applications of this algorithm, we consider industrial cyclic scheduling problems for a reaction-separation network and a tissue paper mill with byproduct recycling. These problems are formulated as MILFP models based on a continuous time Resource-Task Network (RTN). The results show that orders of magnitude reduction in CPU times can be achieved when using this algorithm compared to solving the problems with commercial MINLP solvers.
Computers and Chemical Engineering, 33, 11, 1879-1889.