Abstract
Intelligent decision support systems (DSSs) are popular, helpful, and widely used in many areas requiring sustainability assessment. The purpose of this paper is to demonstrate an intelligent decision support system that enables multi-criteria evaluation, taking into account the temporal dynamics of the performance of the evaluated alternatives. The framework of this DSS is powered by the novel method named DARIA-EDAS (Data vARIability Assessment- Evaluation based on Distance from Average Solution). This temporal multi-criteria decision analysis (MCDA) method enables the evaluation of alternatives with simultaneous consideration of multiple evaluation criteria and the temporal integration of the obtained scores into a single unambiguous score in the form of assessment values and rankings. The performance of the proposed DSS was demonstrated in this research through the example of a multi-criteria temporal assessment of Sustainable Development Goal 7 (SDG 7) implementation by selected European countries. The methodical framework in the proposed DSS provides a rapid, automated, and objective evaluation of a multi-criteria temporal decision-making problem and produces an unambiguous, easy-to-interpret result. The results obtained confirmed the usefulness of the developed DSS in the multi-criteria temporal assessment of sustainable development concerning sustainable energy mix.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdel-Basset, M., Mohamed, R.: A novel plithogenic TOPSIS-CRITIC model for sustainable supply chain risk management. J. Clean. Prod. 247, 119586 (2020). https://doi.org/10.1016/j.jclepro.2019.119586
Baffo, I., Leonardi, M., Bossone, B., Camarda, M.E., D’Alberti, V., Travaglioni, M.: A decision support system for measuring and evaluating solutions for sustainable development. Sustainable Futures 5, 100109 (2023). https://doi.org/10.1016/j.sftr.2023.100109
Beriro, D., et al.: A decision support system to assess the feasibility of onshore renewable energy infrastructure. Renew. Sustain. Energy Rev. 168, 112771 (2022). https://doi.org/10.1016/j.rser.2022.112771
Bouraima, M.B., Ayyıldız, E., Badi, I., Özçelik, G., Yeni, F.B., Pamučar, D.: An integrated intelligent decision support framework for the development of photovoltaic solar power. Eng. Appl. Artif. Intell. 127, 107253 (2024). https://doi.org/10.1016/j.engappai.2023.107253
Cinelli, M., Kadziński, M., Miebs, G., Gonzalez, M., Słowiński, R.: Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system. Eur. J. Oper. Res. 302(2), 633–651 (2022). https://doi.org/10.1016/j.ejor.2022.01.011
Deveci, M., Brito-Parada, P.R., Pamucar, D., Varouchakis, E.A.: Rough sets based ordinal priority approach to evaluate sustainable development goals (SDGs) for sustainable mining. Resour. Policy 79, 103049 (2022). https://doi.org/10.1016/j.resourpol.2022.103049
Frini, A., Amor, S.B.: MUPOM: a multi-criteria multi-period outranking method for decision-making in sustainable development context. Environ. Impact Assess. Rev. 76, 10–25 (2019). https://doi.org/10.1016/j.eiar.2018.11.002
Frini, A., Benamor, S.: Making decisions in a sustainable development context: a state-of-the-art survey and proposal of a multi-period single synthesizing criterion approach. Comput. Econ. 52(2), 341–385 (2018). https://doi.org/10.1007/s10614-017-9677-5
Jiskani, I.M., Cai, Q., Zhou, W., Lu, X., Shah, S.A.A.: An integrated fuzzy decision support system for analyzing challenges and pathways to promote green and climate smart mining. Expert Syst. Appl. 188, 116062 (2022). https://doi.org/10.1016/j.eswa.2021.116062
Krishankumar, R., Pamucar, D., Deveci, M., Ravichandran, K.S.: Prioritization of zero-carbon measures for sustainable urban mobility using integrated double hierarchy decision framework and EDAS approach. Sci. Total Environ. 797, 149068 (2021). https://doi.org/10.1016/j.scitotenv.2021.149068
Maghsoodi, A.I., Riahi, D., Herrera-Viedma, E., Zavadskas, E.K.: An integrated parallel big data decision support tool using the W-CLUS-MCDA: a multi-scenario personnel assessment. Knowl.-Based Syst. 195, 105749 (2020). https://doi.org/10.1016/j.knosys.2020.105749
Torkayesh, A.E., Deveci, M., Karagoz, S., Antucheviciene, J.: A state-of-the-art survey of evaluation based on distance from average solution (EDAS): Developments and applications. Expert Syst. Appl., 119724 (2023). https://doi.org/10.1016/j.eswa.2023.119724
Wątróbski, J., Bączkiewicz, A., Ziemba, E., Sałabun, W.: Sustainable cities and communities assessment using the DARIA-TOPSIS method. Sustain. Urban Areas 83, 103926 (2022). https://doi.org/10.1016/j.scs.2022.103926
Yazdani, M., Pamucar, D., Chatterjee, P., Chakraborty, S.: Development of a decision support framework for sustainable freight transport system evaluation using rough numbers. Int. J. Prod. Res. 58(14), 4325–4351 (2020). https://doi.org/10.1080/00207543.2019.1651945
Acknowledgements
This research was partially funded by National Science Centre, Poland 2022/45/N/HS4/03050, and Co-financed by the Minister of Science under the “Regional Excellence Initiative” Program.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bączkiewicz, A., Wątróbski, J., Jankowski, J., Sałabun, W. (2024). Multi-criteria Temporal Intelligent Decision Support System for Sustainable Energy Mix Assessment. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2024. Lecture Notes in Computer Science(), vol 14796. Springer, Singapore. https://doi.org/10.1007/978-981-97-4985-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-97-4985-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-4984-3
Online ISBN: 978-981-97-4985-0
eBook Packages: Computer ScienceComputer Science (R0)