Scope and Method of Study: This research aimed to the establishment of a general methodological framework, via which the "fuzzy"� and "debatable"� goal of sustainability can be practically achieved in engineering design. In-depth literature review on the sustainability concept was first conducted in an attempt to grasp its philosophical essence from various interpretations and distinct implementations. The application of the proposed framework was addressed by developing or identifying specific building block techniques, each of which accomplish a different task, such as criteria-attribute mapping, preference modeling, and search. The proposed building block techniques were selected based on systematic comparisons among a wide range of alternative methods and tested by case studies or test problems.

Findings and Conclusions: Sustainability is a multiplex property of an integrated system. The key to make a reality of sustainability in engineering design is to properly handle its complex nature and deeply rooted conflicts. In this work, Multiple Criteria Decision Analysis (MCDA) was proven ideal for filling the vacuum of a general operational framework. To implement this framework, a four-step procedure needs to be first performed to formulate a sustainability-oriented design into a "standard"� Multiple Criteria Decision Making (MCDM) problem. The proposed attribute hierarchy "Stressor-Status-Effect-Integrality-Well-being"� and the 4-class metric classification scheme could help engineers to accomplish such a task in the environmental dimension. The achievement of the final "sustainable"� design relies on making appropriate decisions. A MAVT-based technique developed in this study provides a rational and informed way of solving the decision problems with a discrete set of explicitly known alternatives. For Multi-Objective Programming (MOP) problems featuring an infinite and implicitly characterized alternative space, the proposed Ordinal Ranking-based Genetic Algorithm (ORGA) offers a desired searching tool by generating uniformly sampled solutions that are feasible and globally Pareto optimal.