(278a) Embedded Global Optimization for the Synthesis of Sustainable Systems

In this work we introduce a novel embedded global optimization scheme encompassing the dual objectives of identifying where an entity stands in its sustainability efforts and, for a pre-determined desired increase in its sustainability grade, determining a plan of sustainability improvement for that entity that minimizes cost. The embedded problem identifies the entity's current Sustainability Interval Index (SII) via simultaneous global minimization and maximization of an NLP with constraints based on a hierarchical fuzzy-logic assessment scheme similar to [1], [2]. We have discussed the computation of the SII in a previous work [3]. The parent problem is an LP associating a cost for an entity's improvement in each basic indicator, subject to inequality constraints on the entity's target SII derived from its current SII. Numerous case studies illustrating the interplay between the choice of constraints for the embedded problem and the resulting optimal improvement plan will be presented.

[1] Phillis, Yannis A, Kouikoglou, Vassilis S. Fuzzy Measurement of Sustainability. New York: Nova Science Publishers; 2009

[2] Phillis, Yannis A, Davis, Benjamin J. Assessment of Corporate Sustainability via Fuzzy Logic. J Intell Robot Syst. 2009; 55: 3-20

[3] Manousiouthakis, Vasilios I, Phillis, Yannis A, Conner, Jeremy A. On a sustainability interval index and its computation through global optimization, Presented Nov. 9, 2009 at the AIChE Annual Meeting in Nashville, TN.