Abstract

Multi-Criteria Decision Analysis (MCDA) methods have gained popularity among practitioners and researchers in recent years. MCDA methods based on measuring the distance to reference objects are particularly noteworthy since their suitability for most decision problems, comparable and straightforward use, interpretation, and wide post-analytical possibilities. However, software implementations devoted to the MCDA domain show the lack of solutions dedicated to this family containing a sufficient number of methods, providing additional distance metrics and post-analytical tools such as sensitivity analysis. Therefore, this article presents a Python 3 based library that addresses this gap. The research demonstrating the functionalities of the proposed library includes a comparative analysis of the rankings provided by the different methods implemented in the library, a sensitivity analysis of the alternatives to criteria weights modifications, and a robustness analysis of the alternatives to changes in the performance values. The applicability of the proposed library is demonstrated in two real-life numerical examples. The first illustrative example involves the recommendation of renewable energy resources (RES) for development focusing on increasing the significance of RES. The other example involves the evaluation of material suppliers for a steel manufacturing company. Data for both examples were acquired from research papers. Based on the research results, it can be concluded that the proposed library is helpful in the process of supporting the solution of multi-criteria decision problems, and the implementation of a set of methods provides opportunities to search for the most reliable alternative.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.