Philip de Castro We propose a two-stage methodology to support decision-making for large/complex multiobjective optimization problems (MOPs) with a decomposable structure, regardless of whether the MOP, due to its size, is solvable in its entirety. During the first stage, the MOP is decomposed into a collection of multiobjective subproblems each of which have fewer objectives than the original … Read more
Achievement Scalarizing Functions (ASFs) are a class of scalarizing functions for multiobjective optimization problems that have been successfully implemented in many applications due to their mathematical elegance and decision making utility. However, no formal proofs of the fundamental properties of ASFs have been presented in the literature. Furthermore, developments of ASFs, including the construction of … Read more
Many real-life optimization problems need to make decisions with discrete variables and multiple, conflicting objectives. Due to this need, the ability to solve such problems is an important and active area of research. We present a new algorithm, called Pareto Leap, for identifying the (weak) Pareto slices of biobjective mixed-integer programs (BOMIPs), even when Pareto … Read more