An improved decomposition algorithm for optimization under uncertainty

This paper proposes a modification to the decomposition algorithm of Ierape tritou and Pistikopoulos (1994) for process optimization under uncertainty. The key feature of our approach is to avoid imposing constraints on the un certain parameters, thus allowing a more realistic modeling of uncertainty. A theoretical analysis of the earlier algorithm leads to the development o f an improved algorithm which successfully avoids getting trapped in local minima while accounting more accurately for the trade-offs between cost and flexibility. In addition, the improved algorithm is 3-6 times faster, on t he problems tested, than the original one. This is achieved by avoiding the solution of feasibility subproblems, the number of which is exponential in the number of uncertain parameters. (C) 2000 Elsevier Science Ltd. All rig hts reserved.