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All construction projects are subject to constraints, and the final project can be considered to be a single valid solution to a constrained system of interacting parameters from among the various ‘dimensions’ of project management, ie its well known and often cited aspects time, cost, scope (Kerzner, 2009), resources, safety, quality, sustainability, etc. Therefore project managers must be keenly aware of which specific constraints exist, what values they have, and how they impact the particular project. Based on such analysis, they must find efficient and effective ways of steering their projects to remain not only within the region of feasible solutions, but also within it to minimize some parameters and maximize others, depending on their nature. Project management thus can be seen as a multi-objective optimization problem. The question of how to model constraints becomes a significant challenge if one considers that they have very different behaviors, may interact with positive or negative correlation but are not necessarily directly proportional, may be absolute or variable in terms of how strictly they must be followed, and their values may be fixed points or distributed ranges in form of discrete or continuous numbers. Together these factors complicate an accurate and detailed yet intuitive and flexible modeling and analyzing different types of constraints in construction management. Existing approaches for this non-trivial task suffer from being disjointed and