Abstract

A wide range of optimization problems arising in practical applications can be formulated as mixed-integer fractional programming (MIFP) problems, which combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling the non-convex fractional objective function. This talk will explore recent results on the development of tailored global optimization algorithms to address the computational challenge of solving large-scale MIFP problems. These MIFP methods will be illustrated through three applications: (1) function-unit-based life cycle optimization of sustainable product systems and supply chains under sustainability criterions, (2) integrated optimization of production scheduling and process dynamics of continuous multi-grade polymerization reactors, and (3) optimal design and synthesis of energy processes for the production of algal biofuels and bioproducts. For each application problem, we will discuss the modeling framework, solution algorithms, and computational results.