Avoiding simplification strategies by introducing multi-objectiveness in real world problems

Abstract--In business analysis, models are sometimes oversimplified. We pragmatically approach many problems with a single financial objective and include monetary values for non-monetary variables. We enforce constraints which may not be as strict in reality. Based on a case in distributed energy production, we illustrate how we can avoid simplification by modeling multiple objectives, solving it with an NSGAII algorithm with a novel comparison operator. Advantages include a strengthened focus on the trade-off relation between financial and non-monetary objectives. We conclude that this approach is very applicable in a business analysis setting. Keywords-multi-objective evolutionary algorithm, optimizing, constraint satisfaction, real world problems, simplification I. BACKGROUND AND OBJECTIVES When modeling problems in a real world setting, it is often necessary to find the optimum in a set of feasible solutions. In situations where this solution space is (very) large, many model buil...

• Type: Journal
• Year: 2010
• Where: ISDA
• Authors: Charlotte J. C. Rietveld, Gijs P. Hendrix, Frank T. H. M. Berkers, Nadine N. Croes, Selmar K. Smit
• Keywords: ISDA 2010, Operating System, Problems, Simplification, Solution