Recycling/Reuse Systems - Network Growth and Persistence

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

The grant provides funding to develop methodology and tools to improve the material and product flows in collection and processing recycling/reuse networks. Collection system growth and perseverance decisions along with product lifecycles are key determinants of overall system flow potential, and will be modeled via stochastic dynamic programming with an hierarchical approach to plan strategic collection network growth while lower levels determine network operation and logistics. Long term system success requires relationship negotiation among various interdependent collection and processing agents with individual profit objectives and unwillingness to reveal confidential information as products, transportation/processing costs, and markets shift, so a methodology will be developed to determine equilibrium acquisition prices as well as mathematical flow allocation mechanisms between tiers of collectors and processors. Understanding the potential impact of uncertainty on key decisions will be facilitated by determining pivotal scenarios using state of the art methodology for large scale robust optimization. These approaches will be used to develop a deeper understanding of global material flows over time and product lifecycles. Interactions with the carpet and electronic companies will allow these tools to be tested and refined in a variety of contexts. Working with companies, non-profits, and government agencies, the results will enable better decision making for growing collection networks and connecting them with processing capabilities, with practical application to the carpet and electronics industries. The results contribute to the knowledge base for complex supply chain decision-making, both by making the problem representation richer and by developing new approaches for solving large-scale problems in the context of supply chain design. Student researchers and industrial partners will gain exposure to a rich class of problems of significant importance to modern industrialized societies and a set of techniques that can be brought to bear on a wide range of decision-making problems.