Abstract
Multi-Criteria Decision Making (MCDM) methods, such as PROMETHEE II, play a crucial role in complex decision-making scenarios, including team formation. However, they face scalability challenges as the number of criteria and options increases. This paper introduces a novel Hybrid Evolutionary Algorithm integrated with PROMETHEE II, specifically designed for team formation. This hybrid approach combines the exploration power of evolutionary algorithms and the decision-making capabilities of PROMETHEE II, aiming to improve both performance and scalability in decision-making processes. Initial experiments demonstrate significant improvements in both solution quality and scalability compared to existing methods facing similar challenges. This research enables the creation of more efficient and effective team formation in complex decision-making scenarios.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taherdoost, H., Madanchian, M.: Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia 3(1), 77–87 (2023). https://doi.org/10.3390/encyclopedia3010006
Sahoo, S.K., Goswami, S.S.: A comprehensive review of multiple criteria decision-making (MCDM) methods: advancements, applications, and future directions. Decis. Making Adv. 1(1), 25–48 (2023). https://doi.org/10.31181/dma1120237
Dhurkari, R.K.: MCDM methods: practical difficulties and future directions for improvement. RAIRO-Oper. Res. 56(4), 2221–2233 (2022). https://doi.org/10.1051/ro/2022060
Yu, X., Lu, Y., Yu, X.: Evaluating multiobjective evolutionary algorithms using MCDM methods. Math. Probl. Eng. 2018, 1–13 (2018). https://doi.org/10.1155/2018/9751783
Mardani, A., Jusoh, A., Nor, K.M.D., Khalifah, Z., Zakwan, N., Valipour, A.: Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Econ. Res. Ekonomska Istraživanja 28(1), 516–571 (2015). https://doi.org/10.1080/1331677X.2015.1075139
Dadelo, S., Turskis, Z., Zavadskas, E.K., Dadeliene, R.: Multi-criteria assessment and ranking system of sport team formation based on objective-measured values of criteria set. Expert Syst. Appl. 41(14), 6106–6113 (2014). https://doi.org/10.1016/j.eswa.2014.03.036
Brans, J.-P., Mareschal, B.: Promethee methods. In: Greco, S., Ehrgott, M., Figueria, J. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys. ISORMS, vol. 78, pp. 163–186. Springer, New York (2005). https://doi.org/10.1007/0-387-23081-5_5
Dyer, J.S., Fishburn, P.C., Steuer, R.E., Wallenius, J., Zionts, S.: Multiple criteria decision making, multiattribute utility theory: the next ten years. Manage. Sci. 38(5), 645–654 (1992). https://doi.org/10.1287/mnsc.38.5.645
Boix-Cots, D., Pardo-Bosch, F., Pujadas, P.: A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme. Inf. Fusion 96, 16–36 (2023). https://doi.org/10.1016/j.inffus.2023.03.004
Hong, W.-J., Yang, P., Tang, K.: Evolutionary computation for large-scale multi-objective optimization: a decade of progresses. Int. J. Autom. Comput. 18(2), 155–169 (2021). https://doi.org/10.1007/s11633-020-1253-0
Ma, J., Chang, F., Yu, X.: Large-scale evolutionary optimization approach based on decision space decomposition. Front. Energy Res. 10, 926161 (2022). https://doi.org/10.3389/fenrg.2022.926161
Zhang, Y., Tian, Y., Zhang, X.: A comparison study of evolutionary algorithms on large-scale sparse multi-objective optimization problems. In: Ishibuchi, H., et al. (eds.) Evolutionary Multi-Criterion Optimization: 11th International Conference, EMO 2021, Shenzhen, China, March 28–31, 2021, Proceedings, pp. 424–437. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-72062-9_34
Okola, I., Omulo, E.O., Ochieng, D.O., Ouma, G.: A comparison of evolutionary algorithms on a large scale many-objective problem in food–energy–water Nexus. Results Control Optim 10, 100195 (2023). https://doi.org/10.1016/j.rico.2022.100195
de Almeida, A.T., Geiger, M.J., Morais, D.C.: Challenges in multicriteria decision methods. IMA J. Manage. Math. 29(3), 247–252 (2018). https://doi.org/10.1093/imaman/dpy005
Chiu, C.-C., Zhang, S., Lin, J.T., Zhen, L., Huang, E.: Improving the efficiency of evolutionary algorithms for large-scale optimization with multi-fidelity models. In: 2016 Winter Simulation Conference (WSC), pp. 815–826, September 2016. https://doi.org/10.1109/WSC.2016.7822144
Coello, C.A.C., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer US, Boston, MA (2007). https://doi.org/10.1007/978-0-387-36797-2
Behzadian, M., Otaghsara, S., Yazdani, M., Ignatius, J.: A state-of the-art survey of TOPSIS applications. Expert Syst. Appl. 39, 13051–13069 (2012). https://doi.org/10.1016/j.eswa.2012.05.056
Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992). https://doi.org/10.1038/scientificamerican0792-66
Goldberg, L.R.: An alternative ‘description of personality’: The Big-Five factor structure. J. Pers. Soc. Psychol. 59(6), 1216–1229 (1990). https://doi.org/10.1037/0022-3514.59.6.1216
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg (2015). https://doi.org/10.1007/978-3-662-44874-8
Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. Second Edn. Springer, New York (2007)
Gazawa, F.G., Damakoa, I.: An evolutionary algorithm coupled to an outranking method for the multicriteria shortest paths problem. Am. J. Oper. Res. 9(3), 3 (2019). https://doi.org/10.4236/ajor.2019.93007
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evolut. Comput. 11, 712–731 (2008). https://doi.org/10.1109/TEVC.2007.892759
Cannonier, C., Smith, K.: Do crib sheets improve student performance on tests? Evidence from principles of economics. Int. Rev. Econ. Educ. 30, 100147 (2019). https://doi.org/10.1016/j.iree.2018.08.003
Li, M., Kim, D.: One wiki, two groups: dynamic interactions across ESL collaborative writing tasks. J. Second. Lang. Writ. 31, 25–42 (2016). https://doi.org/10.1016/j.jslw.2016.01.002
“USING MYERS-BRIGGS TYPE INDICATOR (MBTI) FOR ASSESSMENT SUCCESS OF STUDENT GROUPS IN PROJECT BASED LEARNING. In: Proceedings of the 2nd International Conference on Computer Supported Education, Valencia, Spain, pp. 156–160. SciTePress - Science and Technology Publications (2010). https://doi.org/10.5220/0002859901560160
Zhang, L., Zhang, X.: Multi-objective team formation optimization for new product development. Comput. Ind. Eng. 64(3), 804–811 (2013). https://doi.org/10.1016/j.cie.2012.12.015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Stavrou, G., Adamidis, P., Papathanasiou, J., Tarabanis, K. (2024). Hybrid Evolutionary Approach to Team Building using PROMETHEE II. In: Campos Ferreira, M., Wachowicz, T., Zaraté, P., Maemura, Y. (eds) Human-Centric Decision and Negotiation Support for Societal Transitions. GDN 2024. Lecture Notes in Business Information Processing, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-031-59373-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-59373-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-59372-7
Online ISBN: 978-3-031-59373-4
eBook Packages: Computer ScienceComputer Science (R0)