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Diagramming qualitative goals for multiobjective project selection in large‐scale systems
Author(s) -
Martinez Lauro J.,
Joshi Nilesh N.,
Lambert James H.
Publication year - 2011
Publication title -
systems engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 50
eISSN - 1520-6858
pISSN - 1098-1241
DOI - 10.1002/sys.20164
Subject(s) - computer science , multi objective optimization , operations research , management science , scale (ratio) , pareto principle , complement (music) , selection (genetic algorithm) , goal programming , hierarchy , mathematical optimization , engineering , mathematics , artificial intelligence , economics , machine learning , physics , phenotype , biochemistry , chemistry , quantum mechanics , complementation , market economy , gene
Allocating resources to competing projects is typically driven by multiple quantified objectives generated from the top‐level goals of a large‐scale system. Analytical tools to aid such allocations have a significant history with many existing methodologies, particularly for optimization and programming within a hierarchy of objective functions. However, the quantified objective functions are known to only partially represent the system goals, and significant challenges remain to preserve relevant considerations that resisted quantification. In particular, the patterns of allocation of resources across the goals may be important to decision‐makers, since they could thereby address known, quantifiable issues with some consideration of unknown and emergent issues. This paper develops decision‐aiding diagrams of top‐level goals and resources that complement the existing multiobjective combinatorial optimization models, to better refine and choose among the optimization‐generated portfolios of projects. Adapting existing path diagrams from the social sciences, the newly developed methodology can be subordinate to the generation of Pareto‐optimal solutions via the optimization model. The application of path diagrams is demonstrated through a case study of allocating resources to a large‐scale system of airports. © 2010 Wiley Periodicals, Inc. Syst Eng 14: 73–86, 2011