(386c) A Multidisciplinary Cyberinfrastructure Approach

Due to increasing pressure for reducing costs and inventories, Enterprise-wide Optimization (EWO) has become a major area of interest in the process industries. Major operational items in EWO include planning, scheduling, real-time optimization and inventory control, with special focus in manufacturing facilities. One of the key features in EWO is integration of the information and decision-making among the various functions that comprise the supply chain of the company. This is being achieved with modern IT tools, which together with the Internet has promoted e-commerce. However, to fully realize the potential of transactional IT tools, the development of sophisticated deterministic and stochastic linear/nonlinear optimization models and algorithms (analytical IT tools) is needed to explore and analyze alternatives of the supply chain to yield overall optimum economic performance, as well as high level of customer satisfaction. An additional challenge is the integrated and coordinated decision-making across the various functions in a company (purchasing, manufacturing, distribution, sales), across various geographically distributed organizations (vendors, facilities and markets), and across various levels of decision-making (strategic, tactical and operational).

In this presentation we describe a multidisciplinary group composed of chemical engineers and operations researchers, who have undertaken a new project in the area of Enterprise-wide Optimization. This project provides an example of a research collaboration that lies at the interface of Cyberinfrastructure with Process Systems Engineering and Operations Research. The major elements of the cyberinfrastructure include capabilities for integration of computational and IT tools, communication among geographically distributed groups, and access to grid computing. We describe software tools for using the grid, and highlight successful applications in solving numerical optimization problems that include optimization under uncertainty and discrete optimization.