To remain healthy in today's competitive environment, chemical companies must operate their supply chain (SC) efficiently by simultaneously optimizing multiple levels of operation. This is a challenging task because supply chains are highly dynamic and interconnected networks, often comprised of different decision makers. Thus, centralized approaches cannot be implemented in practice, while existing decentralized methods yield suboptimal solutions and are insufficient to meet industrial needs. Accordingly, the goal of this project is to develop a cooperation-based framework for supply chain (SC) operation planning that considers local decision-making but at the same time accounts for the interactions between the nodes of the SC. While initial efforts will focus on the industrial gas supply chain, this framework should effectively address operational planning problems in a wide range of manufacturing supply chains.
Intellectual merit: The intellectual merit of this project lies in the discovery, development, and analysis of methods for optimizing large, coupled, networked supply chains using a cooperative distributed framework. In developing this framework the PIs will focus on addressing the following three challenges: 1. Formulate local (node) models as well as models for the entire supply chain that accurately describe the dynamics of and interconnections among nodes of practical supply chains. 2. Develop novel cooperation-based methods for improved decision-making in distributed dynamic supply chains. 3. Assess these methods using real-world data and develop strategies for successful implementation of the methods.
In addressing these challenges, the PIs will combine existing as well as develop new results in the areas of optimization, control theory and game theory. The integration of these results will lead to a novel framework for distributed decision-making that has the potential to transform future supply chains.
Broader Impact: First, the proposed research will improve the efficiency of the industrial gas supply chain, as well as supply chains in other manufacturing sectors, thus increasing the competitiveness of American companies. Second, it has the potential to lead to reductions in a) the overall energy usage in energy intensive sectors such as industrial gases and steel manufacturing, and b) in greenhouse gas emissions. Third, the cooperation-based framework will integrate concepts from optimization, control theory and game theory, thus advancing the state of the art in the area of distributed decision-making. Fourth, the results of this research will be used to develop educational material and will be disseminated through GNU Octave, a freely available dissemination language developed by the group of one of the PIs.
Finally, the research can be transformative because it will quantify the impact of optimizing the overall supply chain by developing tools to facilitate such an analysis and developing methods to show how companies, each with their own financial objectives, can best share the benefit of such collaboration. Such work is needed to transform the operation of energy intensive industries.
