A validation scheme for intelligent and effective multiple criteria decision-making

Applied Soft Computing Journal

Volumn 68, Issue , 2018, Pages 866-872

  Wu, Jei Zheng ^a   Tiao, Pei Jen ^a  

^a SOOCHOW UNIVERSITY   (Taiwan)

Author keywords

Analytic hierarchy process (AHP); elimination et choix traduisant la realit (ELECTRE); Piecewise linear prospect (PLP) theory method; Technique for order preference by similarity to ideal solution (TOPSIS); VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)

Indexed keywords

ANALYTIC HIERARCHY PROCESS; DECISION MAKING; FUNCTION EVALUATION; HIERARCHICAL SYSTEMS; NUMBER THEORY; PARETO PRINCIPLE; PIECEWISE LINEAR TECHNIQUES;

ANALYTIC HIERARCHY PROCESS (AHP); ELECTRE; PIECEWISE LINEAR; TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTIONS; VLSEKRITERIJUMSKA OPTIMIZACIJA I KOMPROMISNO RESENJE (VIKOR);

NUMERICAL METHODS;

EID: 85018887948     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2017.04.054     Document Type: Article

References (57)

1. Lasi, H., Kemper, H.-G., Fettke, P., Feld, T., Hoffmann, M., Industry 4.0. Bus. Inf. Syst. Eng. 6:4 (2014), 239–242.
2. Mosconi, F., The New European Industrial Policy: Global Competitiveness and the Manufacturing Renaissance. 2015, Routledge, London, England.
3. Roblek, V., Meško, M., Krapež, A., A complex view of industry 4.0. SAGE Open 6:2 (2016), 1–11.
4. Wu, J.-Z., Chien, C.-F., Modeling strategic semiconductor assembly outsourcing decisions based on empirical settings. OR Spectr. 30:3 (2008), 401–430.
5. Chien, C.-F., Wu, J.-Z., Weng, Y.-D., Modeling order assignment for semiconductor assembly hierarchical outsourcing and its decision support system. Flexible Serv. Manuf. J. 22:1–2 (2010), 109–139.
6. Wu, J.-Z., Chien, C.-F., Gen, M., Coordinating strategic outsourcing decisions for semiconductor assembly using a bi-objective genetic algorithm. Int. J. Prod. Res. 50:1 (2012), 235–260.
7. Saaty, T.L., Tran, L.T., On the invalidity of fuzzifying numerical judgments in the Analytic Hierarchy Process. Math. Comput. Modell. 46:7–8 (2007), 962–975.
8. Saaty, T.L., The modern science of multicriteria decision making and its practical applications: the AHP/ANP approach. Oper. Res. 61:5 (2013), 1101–1118.
9. Sipahi, S., Timor, M., The analytic hierarchy process and analytic network process: an overview of applications. Manage. Decis. 48:5 (2010), 775–808.
10. Opricovic, S., Tzeng, G.-H., Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156:2 (2004), 445–455.
11. Opricovic, S., Tzeng, G.-H., Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 178:2 (2007), 514–529.
12. Lahdelma, R., Salminen, P., Kuula, M., Testing the efficiency of two pairwise comparison methods in discrete multiple criteria problems. Eur. J. Oper. Res. 145:3 (2003), 496–508.
13. Corrente, S., Greco, S., Słowiński, R., Multiple criteria hierarchy process for ELECTRE Tri methods. Eur. J. Oper. Res. 252:1 (2016), 191–203.
14. Leskinen, P., Kangas, J., Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons. J. Oper. Res. Soc. 56:7 (2005), 855–861.
15. Kahneman, D., Tversky, A., Prospect theory: an analysis of decision under risk. Econometrica 47:2 (1979), 263–291.
16. Anderson, R.M., Clemen, R., Toward an improved methodology to construct and reconcile decision analytic preference judgments. Decis. Anal. 10:2 (2013), 121–134.
17. Köksalan, M., Wallenius, J., Zoints, S., Multiple Criteria Decision Making: From Early History to the 21st Century. 2011, World Scientific Publishing, New Jersey.
18. Stewart, T., Bandte, O., Braun, H., Chakraborti, N., Ehrgott, M., Göbelt, M., Jin, Y., Nakayama, H., Poles, S., Di Stefano, D., Real-world applications of multiobjective optimization, multiobjective optimization. Lecture Notes in Computer Science, vol. 5252, 2008, Springer, Berlin, Heidelberg, 285–327.
19. Ishizaka, A., Nemery, P., Multi-Criteria Decision Analysis: Methods and Software. 2013, John Wiley & Sons, West Sussex.
20. Jacquet-Lagreze, E., Siskos, J., Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. Eur. J. Oper. Res. 10:2 (1982), 151–164.
21. Salminen, P., Solving the discrete multiple criteria problem using linear prospect theory. Eur. J. Oper.l Res. 72:1 (1994), 146–154.
22. Lee, P.T.-W., Wu, J.-Z., Hu, K.-C., Flynn, M., Applying analytic network process (ANP) to rank critical success factors of waterfront redevelopment. Int. J. Shipping Transp. Logist. 5:4/5 (2013), 390–411.
23. Lee, P.T.-W., Wu, J.-Z., Suthiwartnarueput, K., Hu, K.-C., Rodjanapradied, R., A comparative study of key critical factors of waterfront port development: case studies of Incheon Inner Port and Bangkok Port. Growth Change 47:3 (2015), 393–405.
24. Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management. J. Clean. Prod. 16:15 (2008), 1699–1710.
25. Tsai, W.-H., Hsu, W., A novel hybrid model based on DEMATEL and ANP for selecting cost of quality model development. Total Qual. Manage. Bus. Excellence 21:4 (2010), 439–456.
26. Flores, B.E., Whybark, D.C., Multiple criteria ABC analysis. Int. J. Oper. Prod. Manage. 6:3 (1986), 38–46.
27. Flores, B.E., Whybark, D.C., Implementing multiple criteria ABC analysis. J. Oper. Manage. 7:1–2 (1987), 79–84.
28. Flores, B.E., Olson, D.L., Dorai, V.K., Management of multi criteria inventory classification. Math. Comput. Model. 16:12 (1992), 71–82.
29. Ramanathan, R., ABC inventory classification with multiple-criteria using weight linear optimization. Comput. Oper. Res. 33:3 (2006), 695–700.
30. Ng, W.L., A simple classifier for multiple criteria ABC analysis. Eur. J. Oper. Res. 177:1 (2007), 344–353.
31. Hadi-Vencheh, A., An improvement to multiple criteria ABC inventory classification. Eur. J. Oper. Res. 201:3 (2010), 962–965.
32. Soylu, B., Akyol, B., Multi-criteria inventory classification with reference items. Comput. Ind. Eng. 69:1 (2014), 12–20.
33. Yahya, B.N., Wu, J.-Z., Bae, H., Generation of business process reference model considering multiple objectives. Ind. Eng. Manage. Syst. 11:3 (2012), 215–222.
34. Li, B.B., Wang, L., Liu, B., An effective PSO-based hybrid algorithm for multi-objective permutation flow shop scheduling. IEEE Trans. Systems Man Cybern—Part A: Syst. Hum. 38:4 (2008), 818–831.
35. Zinflou, A., Gagné, C., Gravel, M., Price, W.L., Pareto memetic algorithm for multiple objective optimization with an industrial application. J. Heuristics 14:4 (2008), 313–333.
36. Minella, G., Ruiz, R., Ciavotta, M., A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J. Comput. 20:3 (2008), 451–471.
37. Lei, D., Multi-objective production scheduling: a survey. Int. J. Adv. Manuf. Techno. 43:9–10 (2009), 926–938.
38. Zhou, G., Min, H., Gen, M., A genetic algorithm approach to the bi-criteria allocation of customers to warehouses. Int. J. Prod. Econ. 86:1 (2003), 35–45.
39. Gen, M., Green, D., Katai, O., McKay, B., Namatame, A., Sarker, R.A., Zhang, B.-T., Intelligent and Evolutionary Systems. 2009, Springer, Berlin.
40. Meesariganda, B.R., Ishizaka, A., Mapping verbal AHP scale to numerical scale for cloud computing strategy selection. Appl. Soft Comput. 53:April (2017), 111–118.
41. Tyagi, S., Agrawal, S., Yang, K., Ying, H., An extended Fuzzy-AHP approach to rank the influences of socialization-externalization-combination-internalization modes on the development phase. Appl. Soft Comput. 53:March (2017), 505–518.
42. Rudnik, K., Kacprzak, D., Fuzzy TOPSIS method with ordered fuzzy numbers for flow control in a manufacturing system. Appl. Soft Comput. 53:March (2017), 1020–1041.
43. Hong, J., Jeong, K., Xie, Y., A multi-objective approach to planning in emergency logistics network design. Int. J. Ind. Eng.: Theory Appl. Pract. 22:4 (2015), 399–411.
44. Acar, A.Z., Onden, I., Kara, K., Evaluating the location of regional return centers in reverse logistics through integration of GIS, AHP and integer programming. Int. J. Ind. Eng.: Theory Appl.Pract. 22:4 (2015), 399–411.
45. Wang, H.-F., Multicriteria Decision Analysis – From Certainty to Uncertainty. 2004, Ting Lung Book Co, Taipei.
46. Blasco, X., Herrero, J.M., Sanchis, J., Martínez, M., A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Inf. Sci. 178:20 (2008), 3908–3924.
47. Zio, E., Bazzo, R., A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems. Eur. J. Oper. Res. 210:3 (2011), 624–634.
48. Fischer, G.W., Prescriptive decision science: problems and opportunities. Ann. Oper. Res. 19:1 (1989), 489–497.
49. Kendall, M.G., Rank Correlation Methods. 4th ed., 1976, Griffin.
50. McLeod A.I., 2015. Kendall rank correlation and Mann-Kendall trend test, Retrieved June 17, 2015 on World Wide Web: https://cran.r-project.org/web/packages/Kendall/Kendall.pdf.
51. Edwards, W., Barron, F.H., SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Org. Behav. Hum. Decis. Processes 60:3 (1994), 306–325.
52. Velasquez, M., Hester, P.T., An analysis of multi-criteria decision making methods. Int. J. Oper. Res. 10:2 (2013), 56–66.
53. Saaty, T.L., Rank from comparisons and from ratings in the analytic hierarchy/network processes. Eur. J. Oper. Res. 168:2 (2006), 557–570.
54. Shih, H.S., Shyur, H.J., Lee, E.S., An extension of TOPSIS for group decision making. Math. Comput. Modell. 45:7 (2007), 801–813.
55. Aggarwal, M., Discriminative aggregation operators for multi criteria decision making. Appl. Soft Comput. 53:March (2017), 1058–1069.
56. Hatami-Marbini, A., Kangi, F., An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Appl. Soft Comput. 53:March (2017), 1084–1097.
57. Zhao, M., Ma, X., Wei, D., A method considering and adjusting individual consistency and group consensus for group decision making with incomplete linguistic preference relations. Appl. Soft Comput. 53:May (2017), 322–346.