pyMCMA: Uniformly distributed Pareto-front representation

Makowski, M. ORCID:, Granat, J., Shekhovtsov, A., Nahorski, Z., & Zhao, J. (2024). pyMCMA: Uniformly distributed Pareto-front representation. SoftwareX 27 e101801. 10.1016/j.softx.2024.101801.

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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.

Item Type: Article
Uncontrolled Keywords: Pareto-front representation, Multiple-criteria model analysis, Pareto-front visualization, Pyomo modeling language, Structured modeling
Research Programs: Energy, Climate, and Environment (ECE)
Energy, Climate, and Environment (ECE) > Integrated Assessment and Climate Change (IACC)
Energy, Climate, and Environment (ECE) > Sustainable Service Systems (S3)
Depositing User: Luke Kirwan
Date Deposited: 27 Jun 2024 12:56
Last Modified: 27 Jun 2024 12:56

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