An Interior Point Algorithm for Large-Scale Nonlinear Programming
SIAM Journal on Optimization
Flexibility Analysis of Complex Technical Systems under Uncertainty
Automation and Remote Control
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The existing methods of process optimization under uncertain initial information are based on the assumption that in the phase of service known are the exact values of all parameters, which in the phase of design were regarded as partially uncertain. This assumption is too rigid and in practice often is not true. The paper presented new flexibility conditions and a new two-phase problem of optimization with the assumption that in the phase of service the precise values are known only for part of the uncertain parameters. An algorithm was developed to solve the two-phase problem for the case of insufficient experimental information in the process service phase.