Advertisement

Soft Computing

, Volume 23, Issue 1, pp 129–143 | Cite as

ELECTRE-IDAT for design decision-making problems with interval data and target-based criteria

  • Ali JahanEmail author
  • Edmundas Kazimieras Zavadskas
Methodologies and Application
  • 52 Downloads

Abstract

A majority of decision-making problems are accompanied by some kinds of predictions and uncertainties. Therefore, interval data are widely used instead of exact data. The elimination and choice expressing reality methods, referred to as ELECTRE, belong to the outranking methods. Despite their relative complexity, avoiding compensation between criteria is one of the main advantages of the ELECTRE methods. However, no version of ELECTRE methods has the capability to deal with both interval data and target-based criteria. Target-based criteria are applicable in many areas ranging from material selection to medical decision-making problems. Efficiency of the modified ELECTRE method (ELECTRE-IDAT) was examined through two challenging examples. Also, a sensitivity analysis was performed to show advantages of the ELECTRE-IDAT approach. Additionally, the concept of bounded criteria was explained and applicability of interval data as well as benefit, cost, and target criteria were described with a biomaterial selection problem.

Keywords

Design decision-making Uncertainty in data Bounded criteria Target-based criteria Materials and design selection 

Notes

Acknowledgements

This research project was supported by Islamic Azad University, Semnan Branch, with Grant No. 4046, and the author would like to show his grateful thanks for the close cooperation.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Ahn BS (2017) The analytic hierarchy process with interval preference statements. Omega 67:177–185.  https://doi.org/10.1016/j.omega.2016.05.004 Google Scholar
  2. Alemi-Ardakani M, Milani AS, Yannacopoulos S, Shokouhi G (2016) On the effect of subjective, objective and combinative weighting in multiple criteria decision making: a case study on impact optimization of composites. Expert Syst Appl 46:426–438.  https://doi.org/10.1016/j.eswa.2015.11.003 Google Scholar
  3. Amiri M, Nosratian N, Jamshidi A, Kazemi A (2008) Developing a new ELECTRE method with interval data in multiple attribute decision making problems. J Appl Sci 8(22):4017–4028Google Scholar
  4. Bahraminasab M, Jahan A (2011) Material selection for femoral component of total knee replacement using comprehensive VIKOR. Mater Des 32:4471–4477.  https://doi.org/10.1016/j.matdes.2011.03.046 Google Scholar
  5. Balali V, Zahraie B, Roozbahani A (2012) Integration of ELECTRE III and PROMETHEE II decision-making methods with an interval approach: application in selection of appropriate structural systems. J Comput Civil Eng 28(2):297–314Google Scholar
  6. Baradaran V, Azarnia S (2013) An approach to test consistency and generate weights from grey pairwise matrices in grey analytical hierarchy process. J Grey Syst 25(2):46–68Google Scholar
  7. Cables E, Lamata MT, Verdegay JL (2018) FRIM—fuzzy reference ideal method in multicriteria decision making. In: Collan M, Kacprzyk J (eds) Soft computing applications for group decision-making and consensus modeling. Springer, Cham, pp 305–317Google Scholar
  8. Chatterjee P, Athawale VM, Chakraborty S (2009) Selection of materials using compromise ranking and outranking methods. Mater Des 30(10):4043–4053Google Scholar
  9. Chauhan A, Vaish R (2014) A comparative study on decision making methods with interval data. J Comput Eng.  https://doi.org/10.1155/2014/793074 Google Scholar
  10. Chen T-Y (2012) Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: discussions on score functions and weight constraints. Expert Syst Appl 39(2):1848–1861Google Scholar
  11. Chithambaranathan P, Subramanian N, Gunasekaran A, Palaniappan PK (2015) Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. Int J Prod Econ 166:163–176Google Scholar
  12. Datta S, Sahu N, Mahapatra S (2013) Robot selection based on grey-MULTIMOORA approach. Grey Syst Theory Appl 3(2):201–232Google Scholar
  13. Deng J (1982) Control problems of grey systems. Syst Control Lett 1(5):288–294MathSciNetzbMATHGoogle Scholar
  14. Dymova L, Sevastjanov P, Tikhonenko A (2013) A direct interval extension of TOPSIS method. Expert Syst Appl 40(12):4841–4847Google Scholar
  15. Fang Z, Liu S, Shi H, Lin Y (2016) Grey game theory and its applications in economic decision-making. CRC Press, Boca RatonzbMATHGoogle Scholar
  16. Farag MM (1979) Materials and process selection in engineering. Elsevier Science & Technology, LondonGoogle Scholar
  17. Fernández E, Figueira JR, Navarro J (2018) An interval extension of the outranking approach and its application to multiple-criteria ordinal classification. Omega.  https://doi.org/10.1016/j.omega.2018.05.003 Google Scholar
  18. Govindan K, Jepsen MB (2016) ELECTRE: a comprehensive literature review on methodologies and applications. Eur J Oper Res 250(1):1–29MathSciNetzbMATHGoogle Scholar
  19. Hafezalkotob A, Hafezalkotob A (2015) Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Mater Des 87:949–959zbMATHGoogle Scholar
  20. Hafezalkotob A, Hafezalkotob A (2016) Risk-based material selection process supported on information theory: a case study on industrial gas turbine. Appl Soft Comput J.  https://doi.org/10.1016/j.asoc.2016.09.018 zbMATHGoogle Scholar
  21. Hafezalkotob A, Hafezalkotob A (2017) Interval target-based VIKOR method supported on interval distance and preference degree for machine selection. Eng Appl Artif Intell 57:184–196.  https://doi.org/10.1016/j.engappai.2016.10.018 zbMATHGoogle Scholar
  22. Hafezalkotob A, Hafezalkotob A, Sayadi MK (2016) Extension of MULTIMOORA method with interval numbers: an application in materials selection. Appl Math Model 40(2):1372–1386.  https://doi.org/10.1016/j.apm.2015.07.019 MathSciNetzbMATHGoogle Scholar
  23. Hafezalkotob A, Hami-Dindar A, Rabie N, Hafezalkotob A (2018) A decision support system for agricultural machines and equipment selection: a case study on olive harvester machines. Comput Electron Agric 148:207–216.  https://doi.org/10.1016/j.compag.2018.03.012 Google Scholar
  24. Hajiagha SHR, Hashemi SS, Zavadskas EK, Akrami H (2012) Extensions of LINMAP model for multi criteria decision making with grey numbers. Technol Econ Dev Econ 18(4):636–650Google Scholar
  25. Hashemkhani Zolfani S, Sedaghat M, Zavadskas EK (2012) Performance evaluating of rural ICT centers (telecenters), applying fuzzy AHP, SAW-G and TOPSIS grey, a case study in Iran. Technol Econ Dev Econ 18(2):364–387Google Scholar
  26. Heravi G, Fathi M, Faeghi S (2017) Multi-criteria group decision-making method for optimal selection of sustainable industrial building options focused on petrochemical projects. J Clean Prod 142:2999–3013Google Scholar
  27. Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications: a state-of-the-art survey, vol 13. Springer, New YorkzbMATHGoogle Scholar
  28. Jahan A, Bahraminasab M (2015) Multicriteria decision analysis in improving quality of design in femoral component of knee prostheses: influence of interface geometry and material. Adv Mater Sci Eng 2015:16.  https://doi.org/10.1155/2015/693469 Google Scholar
  29. Jahan A, Edwards KL (2013) VIKOR method for material selection problems with interval numbers and target-based criteria. Mater Des 47:759–765.  https://doi.org/10.1016/j.matdes.2012.12.072 Google Scholar
  30. Jahan A, Edwards KL (2015) A state-of-the-art survey on the influence of normalization techniques in ranking: improving the materials selection process in engineering design. Mater Des 65:335–342.  https://doi.org/10.1016/j.matdes.2014.09.022 Google Scholar
  31. Jahan A, Mustapha F, Ismail MY, Sapuan SM, Bahraminasab M (2011) A comprehensive VIKOR method for material selection. Mater Des 32(3):1215–1221.  https://doi.org/10.1016/j.matdes.2010.10.015 Google Scholar
  32. Jahan A, Bahraminasab M, Edwards KL (2012) A target-based normalization technique for materials selection. Mater Des 35:647–654.  https://doi.org/10.1016/j.matdes.2011.09.005 Google Scholar
  33. Jahan A, Edwards KL, Bahraminasab M (2016) Multi-criteria decision analysis for supporting the selection of engineering materials in product design, 2nd edn. Butterworth-Heinemann, OxfordGoogle Scholar
  34. Jahanshahloo GR, Lotfi FH, Izadikhah M (2006) An algorithmic method to extend TOPSIS for decision-making problems with interval data. Appl Math Comput 175(2):1375–1384zbMATHGoogle Scholar
  35. Jahanshahloo GR, Hosseinzadeh Lotfi F, Davoodi AR (2009) Extension of TOPSIS for decision-making problems with interval data: interval efficiency. Math Comput Model 49(5–6):1137–1142MathSciNetzbMATHGoogle Scholar
  36. Jiang Y, Liang X, Liang H (2017) An I-TODIM method for multi-attribute decision making with interval numbers. Soft Comput 21(18):5489–5506zbMATHGoogle Scholar
  37. Khanzadi M, Turskis Z, Ghodrati Amiri G, Chalekaee A (2017) A model of discrete zero-sum two-person matrix games with grey numbers to solve dispute resolution problems in construction. J Civil Eng Manag 23(6):824–835Google Scholar
  38. Kracka M, Zavadskas EK (2013) Panel building refurbishment elements effective selection by applying multiple-criteria methods. Int J Strateg Prop Manag 17(2):210–219Google Scholar
  39. Kumar Sahu A, Datta S, Sankar Mahapatra S (2014) Supply chain performance benchmarking using grey-MOORA approach: an empirical research. Grey Syst Theory Appl 4(1):24–55Google Scholar
  40. Li G-D, Yamaguchi D, Nagai M (2007) A grey-based decision-making approach to the supplier selection problem. Math Comput Model 46(3–4):573–581Google Scholar
  41. Liao H, Wu X, Herrera F (2018) DNBMA: a double normalization-based multi-aggregation method. In: Medina J, Ojeda-Aciego M, Verdegay JL, Perfilieva I, Bouchon-Meunier B, Yager R (eds) Information processing and management of uncertainty in knowledge-based systems. Applications. IPMU 2018. Communications in computer and information science, vol 855. Springer, ChamGoogle Scholar
  42. Liou JJ, Tamošaitienė J, Zavadskas EK, Tzeng G-H (2016) New hybrid COPRAS-G MADM model for improving and selecting suppliers in green supply chain management. Int J Prod Res 54(1):114–134Google Scholar
  43. Liu S, Forrest JYL (2010) Grey systems: theory and applications. Springer, BerlinGoogle Scholar
  44. Liu S, Forrest J, Yang Y (2013) Advances in grey systems research. J Grey Syst 25(2):1–18Google Scholar
  45. Liu H-C, You J-X, Zhen L, Fan X-J (2014) A novel hybrid multiple criteria decision making model for material selection with target-based criteria. Mater Des 60:380–390Google Scholar
  46. Liu S, Yang Y, Xie N, Forrest J (2016a) New progress of grey system theory in the new millennium. Grey Syst Theory Appl 6(1):2–31Google Scholar
  47. Liu S, Yingjie Y, Jeffrey F (2016b) Grey data analysis: methods, models and applications. Springer, SingaporezbMATHGoogle Scholar
  48. Liu S, Yang Y, Forrest J (2017) Grey numbers and their operations grey data analysis: methods, models and applications. Springer, Singapore, pp 29–43zbMATHGoogle Scholar
  49. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11(5):341–356zbMATHGoogle Scholar
  50. Perez EC, Lamata M, Verdegay J (2016) RIM-reference ideal method in multicriteria decision making. Inf Sci 337–338:1–10.  https://doi.org/10.1016/j.ins.2015.12.011 Google Scholar
  51. Rezvani MJ, Jahan A (2015) Effect of initiator, design, and material on crashworthiness performance of thin-walled cylindrical tubes: a primary multi-criteria analysis in lightweight design. Thin Walled Struct 96:169–182.  https://doi.org/10.1016/j.tws.2015.07.026 Google Scholar
  52. Roy B (1991) The outranking approach and the foundations of ELECTRE methods. Theor Decis 31(1):49–73.  https://doi.org/10.1007/bf00134132 MathSciNetGoogle Scholar
  53. Sayadi MK, Heydari M, Shahanaghi K (2009) Extension of VIKOR method for decision making problem with interval numbers. Appl Math Model 33(5):2257–2262MathSciNetzbMATHGoogle Scholar
  54. Sevastianov P (2007) Numerical methods for interval and fuzzy number comparison based on the probabilistic approach and Dempster–Shafer theory. Inf Sci 177(21):4645–4661.  https://doi.org/10.1016/j.ins.2007.05.001 MathSciNetzbMATHGoogle Scholar
  55. Shanian A, Savadogo O (2006) A material selection model based on the concept of multiple attribute decision making. Mater Des 27(4):329–337Google Scholar
  56. Shih HS, Shyur HJ, Lee ES (2007) An extension of TOPSIS for group decision making. Math Comput Model 45(7–8):801–813.  https://doi.org/10.1016/j.mcm.2006.03.023 zbMATHGoogle Scholar
  57. Stanujkic D, Magdalinovic N, Jovanovic R, Stojanovic S (2012) An objective multi-criteria approach to optimization using MOORA method and interval grey numbers. Technol Econ Dev Econ 18(2):331–363zbMATHGoogle Scholar
  58. Stanujkic D, Zavadskas EK, Liu S, Karabasevic D, Popovic G (2017) Improved OCRA method based on the use of interval grey numbers. J Grey Syst 29(4):49–60Google Scholar
  59. Tamošaitienė J, Gaudutis E (2013) Complex assessment of structural systems used for high-rise buildings. J Civil Eng Manag 19(2):305–317Google Scholar
  60. Tavana M, Momeni E, Rezaeiniya N, Mirhedayatian SM, Rezaeiniya H (2013) A novel hybrid social media platform selection model using fuzzy ANP and COPRAS-G. Expert Syst Appl 40(14):5694–5702Google Scholar
  61. Tavana M, Mavi RK, Santos-Arteaga FJ, Doust ER (2016) An extended VIKOR method using stochastic data and subjective judgments. Comput Ind Eng 97:240–247Google Scholar
  62. Turskis Z, Zavadskas EK (2010) A novel method for multiple criteria analysis: grey additive ratio assessment (ARAS-G) method. Informatica 21(4):597–610MathSciNetGoogle Scholar
  63. Turskis Z, Daniūnas A, Zavadskas EK, Medzvieckas J (2016) Multicriteria evaluation of building foundation alternatives. Comput Aided Civil Infrastruct Eng 31(9):717–729Google Scholar
  64. Vafaei N, Ribeiro RA, Camarinha-Matos LM (2016) Normalization techniques for multi-criteria decision making: analytical hierarchy process case study. Paper presented at the doctoral conference on computing, electrical and industrial systemsGoogle Scholar
  65. Vafaei N, Ribeiro RA, Camarinha-Matos LM (2018) Selection of normalization technique for weighted average multi-criteria decision making. Paper presented at the technological innovation for resilient systems: 9th IFIP WG 5.5/SOCOLNET advanced doctoral conference on computing, electrical and industrial systems, DoCEIS 2018, Costa de Caparica, Portugal, May 2–4, 2018, Proceedings 9Google Scholar
  66. Vahdani B, Jabbari AHK, Roshanaei V, Zandieh M (2010) Extension of the ELECTRE method for decision-making problems with interval weights and data. Int J Adv Manuf Technol 50(5):793–800Google Scholar
  67. Wang Y-M, Yang J-B, Xu D-L (2005) A preference aggregation method through the estimation of utility intervals. Comput Oper Res 32(8):2027–2049zbMATHGoogle Scholar
  68. Wu HH (2002) A comparative study of using grey relational analysis in multiple attribute decision making problems. Qual Eng 15(2):209–217Google Scholar
  69. Xie N, Xin J (2014) Interval grey numbers based multi-attribute decision making method for supplier selection. Kybernetes 43(7):1064–1078MathSciNetGoogle Scholar
  70. Yan S-L, Liu S-F, Fang Z-G, Wu L (2014) Method of determining weights of decision makers and attributes for group decision making with interval grey numbers. Syst Eng Theory Pract 34(9):2372–2378Google Scholar
  71. Yue Z, Jia Y (2015) A direct projection-based group decision-making methodology with crisp values and interval data. Soft Comput.  https://doi.org/10.1007/s00500-015-1953-5 zbMATHGoogle Scholar
  72. Zadeh LA (1965) Information and control. Fuzzy Sets 8(3):338–353Google Scholar
  73. Zagorskas J, Zavadskas EK, Turskis Z, Burinskienė M, Blumberga A, Blumberga D (2014) Thermal insulation alternatives of historic brick buildings in Baltic Sea region. Energy Build 78:35–42Google Scholar
  74. Zanakis SH, Solomon A, Wishart N, Dublish S (1998) Multi-attribute decision making: a simulation comparison of select methods. Eur J Oper Res 107(3):507–529zbMATHGoogle Scholar
  75. Zavadskas EK, Kaklauskas A, Turskis Z, Tamošaitienė J (2009) Multi-attribute decision-making model by applying grey numbers. Informatica 20(2):305–320zbMATHGoogle Scholar
  76. Zavadskas EK, Turskis Z, Tamošaitiene J (2010a) Risk assessment of construction projects. J Civil Eng Manag 16(1):33–46Google Scholar
  77. Zavadskas EK, Vilutiene T, Turskis Z, Tamosaitiene J (2010b) Contractor selection for construction works by applying SAW-G and TOPSIS grey techniques. J Bus Econ Manag 11(1):34–55Google Scholar
  78. Zavadskas EK, Turskis Z, Antucheviciene J (2015) Selecting a contractor by using a novel method for multiple attribute analysis: weighted aggregated sum product assessment with grey values (WASPAS-G). Stud Inf Control 24(2):141–150Google Scholar
  79. Zeng Q-L, Li D-D, Yang Y-B (2013) VIKOR method with enhanced accuracy for multiple criteria decision making in healthcare management. J Med Syst 37(2):1–9.  https://doi.org/10.1007/s10916-012-9908-1 Google Scholar
  80. Zhou P, Ang BW, Poh KL (2006) Comparing aggregating methods for constructing the composite environmental index: an objective measure. Ecol Econ 59(3):305–311Google Scholar
  81. Zhou H, Wang J-Q, Zhang H-Y (2017) Grey stochastic multi-criteria decision-making based on regret theory and TOPSIS. Int J Mach Learn Cybern 8(2):651–664.  https://doi.org/10.1007/s13042-015-0459-x Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial Engineering, Semnan BranchIslamic Azad UniversitySemnanIran
  2. 2.Institute of Sustainable ConstructionVilnius Gediminas Technical UniversityVilniusLithuania

Personalised recommendations