An Evolutionary Weight Optimization Framework for Adaptive PROMETHEE-Based Decision Support Systems

Authors

  • Riki Winanjaya Information Systems, STIKOM Tunas Bangsa, Pematangsiantar, North Sumatra, Indonesia,
  • Masri Wahyuni Academy of Informatics and Computer Management, Polibisnis Academy of Informatics and Computer Management, Perdagangan, North Sumatra, Indonesia

DOI:

10.33395/sinkron.v10i4.16860

Keywords:

Multi-Criteria Decision-Making; PROMETHEE; Evolutionary Optimization; Decision Support System; Adaptive MCDM

Abstract

Multi-Criteria Decision-Making (MCDM) methods are widely employed to support complex decision processes involving multiple alternatives and conflicting evaluation criteria. However, the performance of PROMETHEE-based decision support systems is strongly influenced by the criterion weights, which are commonly predetermined and treated as fixed parameters. Such static weighting may limit the adaptability and discriminatory capability of the resulting decision model. This study proposes an Evolutionary Weight Optimization Framework for Adaptive PROMETHEE-Based Decision Support Systems to address this limitation by integrating evolutionary optimization with the PROMETHEE ranking mechanism. The proposed framework treats criterion weights as optimization variables and iteratively searches for an improved weighting configuration before generating the final PROMETHEE ranking. The experimental evaluation was conducted using a dataset comprising 219 alternatives, with the performance of the proposed optimized PROMETHEE compared against a conventional baseline PROMETHEE model using fixed criterion weights. The evaluation focused on net flow distributions, alternative ranking changes, ranking stability, and the discriminatory behavior of the decision-support models. The results demonstrate that weight optimization substantially modifies the preference structure and produces a broader and more differentiated distribution of net flow values compared with the baseline approach. Several alternatives experienced notable ranking improvements or declines, while some alternatives maintained their positions, indicating different levels of sensitivity to the optimized weighting scheme. These findings demonstrate that evolutionary weight optimization can enhance the adaptability and discriminatory capability of PROMETHEE while preserving its interpretable ranking structure. The proposed framework provides a systematic approach for developing more adaptive, robust, and data-responsive MCDM decision support systems.

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How to Cite

Winanjaya, R., & Wahyuni , M. (2026). An Evolutionary Weight Optimization Framework for Adaptive PROMETHEE-Based Decision Support Systems. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 10(4), choir, 2547-2563. https://doi.org/10.33395/sinkron.v10i4.16860

Issue

Vol. 10 No. 4 (2026): Research Articles October 2026

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Articles

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Copyright (c) 2026 Riki Winanjaya, Masri Wahyuni

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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