A broad range of chemical process engineering problems including systematic process design, retrofit design, batch scheduling, production planning, facility layout, and plant startup may be naturally modeled as mixed integer nonlinear programs (MINLP). Although a large number of models have been published for a variety of specific situations, practical application of MINLP methods has been limited because conventional solution techniques are effective only on models of limited size. An important goal of this long term research program is the development of highly parallel algorithms for solving MINLP. In particular, this research seeks to be able to use hundreds of powerful processors to substantially extend the range of tractability for MINLP. The dramatic increase in solution capability is critical in incorporating multiple objectives, realistic physics, flexibility, and robustness into engineering design and operation models. Three important issues that must be addressed in the course of developing effective parallel MINLP algorithms are: (1) the systematic exploitation of problem structure (2) the partitioning of the workload among processors and (3) the utilization of concurrently available information to improve algorithm performance. Central to understanding these issues is the development of special purpose parallel algorithm performance. Central to understanding these issues is the development of special purpose parallel algorithms for highly structured integer programs. Research will proceed along two complementary paths: (1) development of general purpose parallel MINLP algorithms (2) development of special purpose parallel algorithms for highly structured integer programs. The underlying goal of both paths is to learn how to effectively use massive amounts of parallelism while at the same time exploiting problem specific features.
