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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/106742 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/106742 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/106742 archive 353 disk0/00/01/98/40 2024-06-27 12:56:07 2024-06-27 12:56:07 2024-06-27 12:56:07 article show Makowski M. 1254 0000-0002-6107-0972 Granat J. Shekhovtsov A. Nahorski Z. Zhao J. pub ECE IACC S3 Pareto-front representation, Multiple-criteria model analysis, Pareto-front visualization, Pyomo modeling language, Structured modeling pyMCMA is the Python implementation of a novel method for autonomous computation of the Pareto-front representation composed of efficient solutions distributed uniformly in terms of distances between neighbor Pareto solutions. pyMCMA supports scientific, i.e. objective, model analysis by providing preference-free Pareto front representation. pyMCMA seamlessly integrates independently developed substantive models. The computed Pareto-front, also for more than two criteria, is visualized by interactive parallel coordinate plot, as well as by charts of criteria pairs. Moreover, pyMCMA optionally exports the results for problems-specific analysis in the substantive model’s variables space. The pyMCMA functionality is illustrated by an analysis of China’s liquid fuel production model. 2024-09 published Elsevier doi:10.1016/j.softx.2024.101801 193 public SoftwareX 27 e101801 TRUE 2352-7110 no info:eu-repo/semantics/article