GOALI: Global Meta-Hybrids for Supply Chain Optimization

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

This is a Grant Opportunities for Academic Liaison with Industry (GOALI) award. The goal of the project is to develop global meta-hybrid approaches and corresponding software tools for the efficient solution of large-scale supply chain optimization applications and other massive combinatorial optimization problems. This research effort is built on recent research in heuristics for combinatorial optimization, including the global meta-heuristic Nested Partitions (NP) framework. The focus is on problem classes for which fast heuristics may be developed for both the construction of feasible solutions and for the improvement of such solutions. However, rather than considering heuristics in isolation, the researchers wish to obtain maximum benefit from their availability by employing them within the global framework of partition-based strategies. The term global meta-hybrid is used to denote the combination of a global meta-heuristic with linear programming technology. Such a hybrid combines the lower bound information available from linear programming relaxations with the upper bound and feasible solution data from the heuristic in a synergistic fashion. This project will yield mathematical techniques and corresponding computer software for the generation of high-quality solutions for large-scale supply chain models as well as large-scale combinatorial optimization problems arising from other applications such as radiotherapy. Such problems are intractable for current approaches that do not take advantage of the special properties to exploit within the methodological framework. This project is a collaboration with Rockwell Automation.