SBIR-STTR Award

Economic pressures and global competition are forcing US manufacturers and government to make decisions as quickly and cost effectively as possible. Such decision problems include locating facilities, transporting manpower and materials, battle strategy analysis, globally coordinating manufacturing, and molecular design. We propose commercialization of the distributed decision support systems DOMINION, which will make decisions using rigorous optimization principles rather than rule-based or other heuristic approaches currently in practice, allowing for the most cost effective decisions problems currently require expensive supercomputers to exploit rigorous optimization technology, DOMINION will be developed for much lower cost platforms (desktop workstations) in large number. This will allow organizations without large scale computers to utilize their currently installed workstations and networks instead. It further removes the single point of failure fault from large, time critical computations. DOMINION will operate by distributing a parallelizable optimization problem across a heterogeneous, globally distributed network of workstations. Each computational agent in the distribution is fully capable of solving portions of the overall problem and dispatching portions of its work to and receiving from other networked agents. This model, in contrast to the currently popular master-slave models, is easily scalable to very large networks, and is significantly more robust to partial network failure. Sales of decision support software easily range in the hundreds of millions of dollars per year. DOMINION will augment the current commercial offerings with new capabilities, and allow optimization of larger decision problems at a much lower cost.

Keywords:
Distributed Computing Optimization Mathematical Programming Discrete Optimization

Economic pressures and global competition are forcing U.S. manufacturers and the government to make decisions as quickly and cost effectively as possible. Decision problems include locating facilities, transporting manpower and material battle strategy analysis, coordinating manufacturing, and molecular design. We propose commercialization of the distributed decision support system DOMINION, which will make decision using rigorous optimization principles rather than heuristic approaches currently in practice. DOMINION will be packaged as a framework to construct customized decision support software, as an optimization tool to replace sequential products, and as the computational engine for our chemical plant scheduling and design software. Availability of DOMINION on desktop workstation networks will permit its use by organizations without large scale computers. However, we will also provide a transparent migration path to massively parallel computers for the largest scale applications. DOMINION will operate by distributing a parallelizable optimization problem across a heterogeneous, wide area network. Each computational agent in the distribution is fully capable of solving portions of the overall problem and dispatching portions of its work to and receiving from other networked agents. This model in contrast to the currently popular master-slave models, is easily scaleable to large networks, and is significantly more robust to failure. Sales of decision support software easily range in the hundreds of millions of dollars per year. DOMINION will augment the current commercial offerings with new capabilities, and allow optimization of larger decision problems at a lower cost. DOMINION will serve as a platform for quickly constructing customized, distributed decision support Systems that will operate in computing environments ranging from a collection of workstations to massively parallel Computers. Initially, we expect DOMINION to enter the commercial market in an optimization environment that can be used by anyone currently utilizing sequential decision support technology. As an example, we will integrate DOMINION into our own software for chemical plant scheduling and design to provide di

Keywords:
Distributed Computing Combinatorial Optimization Decision Support Design/Scheduling