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Integrated Ranking Algorithm for Efficient Decision Making

    https://doi.org/10.1142/S0219622021500152Cited by:9 (Source: Crossref)
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    Abstract

    Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

    References

    • 1. B. Roy, The outranking approach and the foundations of electre methods, in Readings in Multiple Criteria Decision Aid (Springer, 1990), pp. 155–183. Crossref, Google Scholar
    • 2. E. Jacquet-Lagreze and J. Siskos, Assessing a set of additive utility functions for multicriteria decision-making, the UTA method, European Journal of Operational Research 10 (1982) 151–164. Crossref, ISI, Google Scholar
    • 3. R. Benayoun, J. De Montgolfier, J. Tergny and O. Laritchev, Linear programming with multiple objective functions: Step method (STEM), Mathematical Programming 1 (1971) 366–375. Crossref, Google Scholar
    • 4. N. Deepa, K. Ganesan, K. Srinivasan and C.-Y. Chang, Realizing sustainable development via modified integrated weighting MCDM model for ranking agrarian dataset, Sustainability 11 (2019) 6060. Crossref, ISI, Google Scholar
    • 5. S. Amirshenava and M. Osanloo, Post-mining land-use selection by using a combination of promethee and sir techniques, in Land Reclamation in Ecological Fragile Areas, (CRC Press, 2017), pp. 61–69. Crossref, Google Scholar
    • 6. K. P. Vishnunarayanan, R. Shreekrishna Prasad and A. Umamakeswari, Resume classification using analytic hierarchy process and keyword, International Journal of Pure and Applied Mathematics 115 (2017) 43–48. Google Scholar
    • 7. A. J. Ruby, B. W. Aisha and C. P. Subash, Comparison of multi criteria decision making algorithms for ranking cloud renderfarm services, arXiv:1611.10204. Google Scholar
    • 8. J. Godlewska and E. Sidorczuk-Pietraszko, Taxonomic assessment of transition to the green economy in polish regions, Sustainability 11 (2019) 5098. Crossref, ISI, Google Scholar
    • 9. N. Deepa and K. Ganesan, Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection, Soft Computing 23 (2018) 1–17. ISI, Google Scholar
    • 10. N. Deepa, K. Ganesan and B. Sethuramasamyraja, Predictive mathematical model for solving multi-criteria decision-making problems, Neural Computing and Applications 31 (2018) 1–14. ISI, Google Scholar
    • 11. A. B. Leoneti, Considerations regarding the choice of ranking multiple criteria decision making methods, Pesquisa Operacional 36 (2016) 259–277. Crossref, Google Scholar
    • 12. P. Taillandier and S. Stinckwich, Using the promethee multi-criteria decision making method to define new exploration strategies for rescue robots, in 2011 IEEE Int. Symp. Safety, Security, and Rescue Robotics (Kyoto, Japan, 2011). Crossref, Google Scholar
    • 13. N. Deepa and K. Ganesan, Multi-class classification using hybrid soft decision model for agriculture crop selection, Neural Computing and Applications 30 (2018) 1025–1038. Crossref, ISI, Google Scholar
    • 14. N. Deepa and K. Ganesan, Decision-making tool for crop selection for agriculture development, Neural Computing and Applications 31 (2019) 1215–1225. Crossref, ISI, Google Scholar
    • 15. N. Deepa et al., An efficient ensemble vtopes multi-criteria decision-making model for sustainable sugarcane farms, Sustainability 11 (2019) 4288. Crossref, ISI, Google Scholar
    • 16. T. Vulević and N. Dragović, Multi-criteria decision analysis for sub-watersheds ranking via the PROMETHEE method, International Soil and Water Conservation Research 5 (2017) 50–55. Crossref, ISI, Google Scholar
    • 17. N. Jaini and S. Utyuzhnikov, A fuzzy trade-off ranking method for multi-criteria decision-making, Axioms 7 (2018) 1. Crossref, ISI, Google Scholar
    • 18. G. Kou, Y. Lu, Y. Peng and Y. Shi, Evaluation of classification algorithms using MCDM and rank correlation, International Journal of Information Technology & Decision Making 11 (2012) 197–225. Link, ISI, Google Scholar
    • 19. L. Karlitasari, D. Suhartini and L. Nurrosikawati, Implementation of preference ranking organization method for enrichment evaluation (Promethee) on selection system of student’s achievement, in IOP Conference Series: Materials Science and Engineering, Vol. 332 (Tangerang Selatan, Indonesia, 2018). Crossref, Google Scholar
    • 20. A. Debnath, J. Roy, S. Kar, E. Zavadskas and J. Antucheviciene, A hybrid MCDM approach for strategic project portfolio selection of agro by-products, Sustainability 9 (2017) 1302. Crossref, ISI, Google Scholar
    • 21. J. Tamošaitienė, E. K. Zavadskas, I. Šileikaitė and Z. Turskis, A novel hybrid MCDM approach for complicated supply chain management problems in construction, Procedia Engineering 172 (2017) 1137–1145. Crossref, Google Scholar
    • 22. M. K. Ghorabaee, M. Amiri, E. K. Zavadskas and J. Antucheviciene, A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations, Archives of Civil and Mechanical Engineering 18 (2018) 32–49. Crossref, ISI, Google Scholar
    • 23. E. Yadegaridehkordi et al., Development of a SaaS adoption decision-making model using a new hybrid MCDM approach, International Journal of Information Technology & Decision Making 18 (2019) 1845–1874. Link, ISI, Google Scholar
    • 24. M.-F. Chen, G.-H. Tzeng and T.-I. Tang, Fuzzy MCDM approach for evaluation of expatriate assignments, International Journal of Information Technology & Decision Making 4 (2005) 277–296. Link, ISI, Google Scholar
    • 25. B. Caroleo et al., A knowledge-based multi-criteria decision support system encompassing cascading effects for disaster management, International Journal of Information Technology & Decision Making 17 (2018) 1469–1498. Link, ISI, Google Scholar
    • 26. A. Hinduja et al., An integrated intuitionistic fuzzy MCDM approach to select cloud-based ERP system for SMEs, International Journal of Information Technology & Decision Making 18 (2019) 1875–1908. Link, ISI, Google Scholar
    • 27. S. H. Zolfani, M. Pourhossein, M. Yazdani and E. K. Zavadskas, Evaluating construction projects of hotels based on environmental sustainability with MCDM framework, Alexandria Engineering Journal 57 (2018) 357–365. Crossref, ISI, Google Scholar
    • 28. W. G. Stillwell, D. A. Seaver and W. Edwards, A comparison of weight approximation techniques in multiattribute utility decision making, Organizational Behavior and Human Performance 28 (1981) 62–77. Crossref, ISI, Google Scholar
    • 29. D. Julong, Introduction to grey system theory, The Journal of Grey System 1 (1989) 1–24. Google Scholar
    • 30. J.-P. Brans, P. Vincke and B. Mareschal, How to select and how to rank projects: The PROMETHEE method, European Journal of Operational Research 24 (1986) 228–238. Crossref, ISI, Google Scholar
    • 31. T. Özcan, N. Çelebi and Ş. Esnaf, Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem, Expert Systems with Applications 38 (2011) 9773–9779. Crossref, ISI, Google Scholar
    • 32. S. Liu, C. Guo, F. Al-Turjman, K. Muhammad and V. H. C. de Albuquerque, Reliability of response region: A novel mechanism in visual tracking by edge computing for IIoT environments, Mechanical Systems and Signal Processing 138 (2020) 106537. Crossref, ISI, Google Scholar
    • 33. S. Liu, G. Liu and H. Zhou, A robust parallel object tracking method for illumination variations, Mobile Networks and Applications 24 (2019) 5–17. Crossref, ISI, Google Scholar
    • 34. S. Liu, W. Bai, G. Liu, W. Li and H. M. Srivastava, Parallel fractal compression method for big video data, Complexity 2018 (2018). Google Scholar
    • 35. J. H. Zar, Significance testing of the Spearman rank correlation coefficient, Journal of the American Statistical Association 67 (1972) 578–580. Crossref, ISI, Google Scholar
    • 36. C. Lin, G. Kou, Y. Peng and F. E. Alsaadi, Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM, Annals of Operations Research 30 (2020) 1–17. Google Scholar
    • 37. G. Kou, D. Ergu, C. Lin and Y. Chen, Pairwise comparison matrix in multiple criteria decision making, Technological and Economic Development of Economy 22 (2016) 738–765. Crossref, ISI, Google Scholar
    • 38. G. Kou and C. Lin, A cosine maximization method for the priority vector derivation in AHP, European Journal of Operational Research 235 (2014) 225–232. Crossref, ISI, Google Scholar
    • 39. G. Kou, D. Ergu and J. Shang, Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction, European Journal of Operational Research 236 (2014) 261–271. Crossref, ISI, Google Scholar
    • 40. X. Chao, G. Kou, Y. Peng and E. H. Viedma, Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: An application in financial inclusion, European Journal of Operational Research 288(1) (2021) 271–293. Crossref, ISI, Google Scholar