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Large-scale multiple criteria decision-making with missing values: project selection through TOPSIS-OPA

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Abstract

Nowadays, with the development of information management infrastructures in organizations and the improvement of the data storage process, managers are looking for appropriate decision-making methods based on large volumes of data. Therefore, it is crucial to choose the right approach to make the right decisions based on the volume of available data. The present study seeks to provide a comprehensive framework for the decision-making process using big data, even when it is incomplete. The framework of multiple criteria decision making (MCDM) consists of criteria and alternatives, whereas in real-world cases, decision-makers may face several criteria and alternatives. In this study, the Principal Component Analysis (PCA) approach was selected for the criteria clustering. Later, the K-means algorithm is used to cluster the alternatives, which estimates the optimal number of clusters using the Elbow method. The Fuzzy TOPSIS (TOPSIS-F) and Ordinal Priority Approach (OPA) have been used to rank clusters. Ultimately, the best alternative in the top cluster has been identified with the aid of the OPA, which has a unique function to solve MCDM problems with incomplete data. For evaluating the performance of the proposed approach, first, a pilot testing has been executed on a real-world case, and then a practical study was conducted at a refinery equipment manufacturing company with a project-oriented organizational structure. The approach is flexible, interactive, intelligent, and integrative, and significantly reduces the time and computation costs for the decision-makers. The results confirmed the soundness of the proposed approach, which can be used by managers of different companies with confidence.

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References

  • Ait-Mlouk A, Agouti T (2019) DM-MCDA: a web-based platform for data mining and multiple criteria decision analysis: a case study on road accident. SoftwareX 10:100323

    Article  Google Scholar 

  • Ataei Y, Mahmoudi A, Feylizadeh MR, Li DF (2020) Ordinal priority approach (OPA) in multiple attribute decision-making. Appl Soft Compu J 86:105893

    Article  Google Scholar 

  • Bao F (2020) “Best_kmeans(X).” MATLAB central file exchange. https://www.mathworks.com/matlabcentral/fileexchange/49489-best_kmeans-x. Accessed 8 Sept 2020

  • Bumblauskas D, Gemmill D, Igou A, Anzengruber J (2017) Smart maintenance decision support systems (SMDSS) based on corporate big data analytics. Expert Syst Appl 90:303–317

    Article  Google Scholar 

  • Cerny BA, Kaiser HF (1977) A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivar Behav Res 12(1):43–47

    Article  Google Scholar 

  • Chen CT (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9

    Article  MATH  Google Scholar 

  • De Tré G, De Mol R, Bronselaer A (2018) Handling veracity in multi-criteria decision-making: a multi-dimensional approach. Inf Sci 460–461:541–554

    Article  Google Scholar 

  • Duan Y, Edwards JS, Dwivedi YK (2019) Artificial intelligence for decision making in the era of big data—evolution, challenges and research agenda. Int J Inf Manag 48:63–71

    Article  Google Scholar 

  • Holland SM (2008) Principal components analysis (PCA). University of Georgia, Athens

    Google Scholar 

  • Hwang CL, Lai YJ, Liu TY (1993) A new approach for multiple objective decision making. Comput Oper Res 20(8):889–899

    Article  MATH  Google Scholar 

  • Ijadi Maghsoodi A, Kavian A, Khalilzadeh M, Brauers WK (2018) CLUS-MCDA: a novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem. Comput Ind Eng 118:409–422

    Article  Google Scholar 

  • Javed SA, Liu S (2019) Bidirectional absolute GRA/GIA model for uncertain systems: application in project management. IEEE Access 7:60885–60896

    Article  Google Scholar 

  • Javed SA, Mahmoudi A, Liu S (2020) Grey absolute decision analysis (GADA) method for multiple criteria group decision making under uncertainty. Int J Fuzzy Syst 22:1073–1090

    Article  Google Scholar 

  • Jiménez A, Mateos A, Ríos-Insua S (2009) Missing consequences in multiattribute utility theory. Omega 37(2):395–410

    Article  Google Scholar 

  • Kaiser HF (1974) An index of factorial simplicity. Psychometrika 39(1):31–36

    Article  MATH  Google Scholar 

  • Kannan SR, Ramathilagam S, Chung PC (2012) Effective fuzzy C-means clustering algorithms for data clustering problems. Expert Syst Appl 39(7):6292–6300

    Article  Google Scholar 

  • Kashani AG, Graettinger AJ (2015) Cluster-based roof covering damage detection in ground-based lidar data. Autom Constr 58:19–27

    Article  Google Scholar 

  • Kauffmann E, Peral J, Gil D, Ferrández A, Sellers R, Mora H (2019) A framework for big data analytics in commercial social networks: a case study on sentiment analysis and fake review detection for marketing decision-making. Ind Mark Manag 90:523–537

    Article  Google Scholar 

  • Ketchen DJ, Shook CL (1996) The application of cluster analysis in strategic management research: an analysis and critique. Strategy Manag J 17(6):441–458

    Article  Google Scholar 

  • Kourtit K, Nijkamp P (2018) Big data dashboards as smart decision support tools for I-cities—an experiment on Stockholm. Land Use Policy 71:24–35

    Article  Google Scholar 

  • Kurilovas E (2018) On data-driven decision-making for quality education. Comput Hum Behav 107:105774

    Article  Google Scholar 

  • Lai Y-J, Liu T-Y, Hwang C-L (1994) TOPSIS for MODM. Eur J Oper Res 76(3):486–500

    Article  MATH  Google Scholar 

  • Lever J, Krzywinski M, Altman N (2017) Points of significance: principal component analysis. Nat Methods 14(7):641–642

    Article  Google Scholar 

  • Liou JJH, Chuang YC, Zavadskas EK, Tzeng GH (2019) Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement. J Clean Prod 241:118321

    Article  Google Scholar 

  • Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  MATH  Google Scholar 

  • Ma W, Xiong W, Luo X (2013) A model for decision making with missing, imprecise, and uncertain evaluations of multiple criteria. Int J Intell Syst 28(2):152–184

    Article  Google Scholar 

  • Mahmoudi A, Deng X, Javed SA, Zhang N (2020a) Sustainable supplier selection in megaprojects: grey ordinal priority approach. Bus Strategy Environ. https://doi.org/10.1002/bse.2623

    Article  Google Scholar 

  • Mahmoudi A, Javed SA, Mardani A (2020b) Gresilient supplier selection through fuzzy ordinal priority approach: decision-making in post-COVID era. Oper Manag Res (in press)

  • Mahmoudi A, Mi X, Liao H, Feylizadeh MR, Turskis Z (2020c) Grey best-worst method for multiple experts multiple criteria decision making under uncertainty. Informatica 31(2):331–357

    Article  MathSciNet  Google Scholar 

  • Manly BFJ (2005) Multivariate statistical methods: a primer. Chapman & Hall/CRC Press, Boca Raton

    MATH  Google Scholar 

  • Mardani A, Jusoh A, Zavadskas EK (2015) Fuzzy multiple criteria decision-making techniques and applications—two decades review from 1994 to 2014. Expert Syst Appl 42(8):4126–4148

    Article  Google Scholar 

  • Olander S, Landin A (2005) Evaluation of stakeholder influence in the implementation of construction projects. Int J Project Manag 23(4):321–328

    Article  Google Scholar 

  • Opricovic S, Tzeng G-H (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455

    Article  MATH  Google Scholar 

  • Palczewski K, Sałabun W (2019) Influence of various normalization methods in PROMETHEE II: an empirical study on the selection of the airport location. Procedia computer science, vol 159. Elsevier B.V., Amsterdam, pp 2051–2060

  • Pedrycz W (1994) Why triangular membership functions? Fuzzy Sets Syst 64(1):21–30

    Article  MathSciNet  Google Scholar 

  • Peng Y, Zhang Y, Kou G, Shi Y (2012) A multicriteria decision making approach for estimating the number of clusters in a data set. PLoS ONE 7(7):e41713

    Article  Google Scholar 

  • Peng DH, Peng B, Wang TD (2019) Reconfiguring IVHF-TOPSIS decision making method with parameterized reference solutions and a novel distance for corporate carbon performance evaluation. J Ambient Intell Humaniz Comput 11(9):3811–3832

    Article  Google Scholar 

  • Preetha R, Vinila Jinny S (2020) Early diagnose breast cancer with PCA-LDA based FER and neuro-fuzzy classification system. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02395-z

    Article  Google Scholar 

  • Ralambondrainy H (1995) A conceptual version of the K-means algorithm. Pattern Recogn Lett 16(11):1147–1157

    Article  Google Scholar 

  • Rencher AC, Christensen William F (2012) Methods of multivariate analysis, 3rd edn. Wiley, Hoboken

    Book  MATH  Google Scholar 

  • Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Article  Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26

    Article  MATH  Google Scholar 

  • Sattarpour T, Nazarpour D, Golshannavaz S, Siano P (2018) A multi-objective hybrid GA and TOPSIS approach for sizing and siting of DG and RTU in smart distribution grids. J Ambient Intell Humaniz Comput 9(1):105–122

    Article  Google Scholar 

  • Sharif Ullah AMM, Noor-E-Alam M (2018) Big data driven graphical information based fuzzy multi criteria decision making. Appl Soft Comput J 63:23–38

    Article  Google Scholar 

  • Sharma DK, Dhurandher SK, Agarwal D, Arora K (2019) KROp: k-means clustering based routing protocol for opportunistic networks. J Ambient Intell Humaniz Comput 10(4):1289–1306

    Article  Google Scholar 

  • Sidhu J, Singh S (2017) Design and comparative analysis of MCDM-based multi-dimensional trust evaluation schemes for determining trustworthiness of cloud service providers. J Grid Comput 15(2):197–218

    Article  Google Scholar 

  • Tang M, Liao H (2019) From conventional group decision making to large-scale group decision making: what are the challenges and how to meet them in big data era? A state-of-the-art survey. Omega. https://doi.org/10.1016/j.omega.2019.102141

    Article  Google Scholar 

  • Thorndike RL (1953) Who belongs in the family? Psychometrika 18(4):267–276

    Article  Google Scholar 

  • Witten IH, Frank E, Hall MA, Pal CJ (2011) Data mining: practical machine learning tools and techniques. The Morgan Kaufmann Series in Data Management Systems

  • Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  Google Scholar 

  • Wu T, Liu XW (2016) An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems. Knowl Based Syst 114:118–127

    Article  Google Scholar 

  • Yadegaridehkordi E, Hourmand M, Nilashi M, Shuib L, Ahani A, Ibrahim O (2018) Influence of big data adoption on manufacturing companies’ performance: an integrated DEMATEL-ANFIS approach. Technol Forecast Soc Change 137:199–210

    Article  Google Scholar 

  • Yasin Ali M, Sultana A, Khodadad Khan AFM (2016) Comparison of fuzzy multiplication operation on triangular fuzzy number. IOSR J Math 12(4):35–41

    Article  Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China under Grant No. NSFC-71771052. The work presented in this paper corresponds to the doctoral dissertation of the first author at the Southeast University, China. More information regarding the ordinal priority approach (OPA) can be found at www.ordinalpriorityapproach.com. The authors would like to thank Mr Mostafa Parvizi from FSK company for his support and cooperation.

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Authors and Affiliations

  1. Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing, 210096, China

    Amin Mahmoudi, Xiaopeng Deng & Jingfeng Yuan

  2. School of Business, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Saad Ahmed Javed

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  1. Amin Mahmoudi
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  2. Xiaopeng Deng
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  3. Saad Ahmed Javed
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  4. Jingfeng Yuan
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Correspondence to Xiaopeng Deng.

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Mahmoudi, A., Deng, X., Javed, S.A. et al. Large-scale multiple criteria decision-making with missing values: project selection through TOPSIS-OPA. J Ambient Intell Human Comput 12, 9341–9362 (2021). https://doi.org/10.1007/s12652-020-02649-w

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