Tools
Tools
Makowski, M. 
ORCID: https://orcid.org/0000-0002-6107-0972, Granat, J., Shekhovtsov, A., Nahorski, Z., & Zhao, J.
(2024).
pyMCMA: Uniformly distributed Pareto-front representation.
SoftwareX 27 e101801. 10.1016/j.softx.2024.101801.
Preview |
Text
1-s2.0-S2352711024001729-main.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
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 |
| URI: | https://pure.iiasa.ac.at/19840 |
Actions (login required)
 |
View Item |