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Analytic Hierarchy Process (AHP) in Maritime Logistics: Theory, Application and Fuzzy Set Integration

  • Emrah Bulut
  • Okan Duru
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 260)

Abstract

In the last few decades, there is a growing interest in using Analytic Hierarchy Process (henceforth AHP), and it is frequently employed in solving the maritime industry problem since the 2000s. The AHP method is a powerful instrument to decompose complex decision-making problems and to simplify (facilitate) decision makers’ cognitive burden. In contrast to its predecessors, AHP is capable of executing both hard and soft information (i.e. numerical data/input and subjective/judgemental assessment respectively) through a top-down investigation of micro aspects in each level of the hierarchy. Although AHP is very functional and popular in both academia and professional life, there are various biases and misuse of the method which are heavily based on the lack of theoretical basis. The AHP method has several underlying assumptions, and each assumption needs to be investigated and demonstrated through specific decision making problems. Ignoring these fundamentals of AHP eventually initiates various forms of inconsistencies and sometimes implicit invalidity which is difficult to detect from derived results. In this chapter, the theory of AHP will be discussed in detail with references to other theories in social sciences and its practical impacts on the AHP analysis. In addition to the conventional AHP methodology, the fuzzy set extension (Fuzzy AHP or FAHP) and its rationale in particular problems will be investigated. Empirical applications will help clarifying its capability of solving some maritime and logistics problems while developing hands-on experience with numerical examples.

Keywords

Analytic Hierarchy Process Fuzzy logic Decision theory Rational choice theory Consistency control 

References

  1. Aguarón J, Moreno-Jiménez JM (2003) The geometric consistency index: approximated thresholds. Eur J Oper Res 147(1):137–145CrossRefGoogle Scholar
  2. Akao Y, Mazur GH (2003) The leading edge in QFD: past, present and future. Int J Qual Reliab Manag 20(1):20–35CrossRefGoogle Scholar
  3. Badiru AB, Cheung J (2002) Fuzzy engineering expert systems with neural network applications. Wiley, New YorkGoogle Scholar
  4. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst 17(3):233–247CrossRefGoogle Scholar
  5. Bulut E, Duru O, Kececi T, Yoshida S (2012) Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: a process model for shipping asset management. Expert Syst Appl 39:1911–1923CrossRefGoogle Scholar
  6. Bulut E, Duru O, Koçak G (2014) Rotational priority investigation in fuzzy analytic hierarchy process design: an empirical study on the marine engine selection problem. Appl Math Model 39(2):913–923CrossRefGoogle Scholar
  7. Bulut E, Duru O, Yoshida S (2010) Multi-attribute decision making for crew nationality pattern selection in the shipping business: an empirical study for Turkish shipping case. Asian J Shipp Logist 26(1):139–152CrossRefGoogle Scholar
  8. Bulut E, Duru O, Yoshida S (2013) Market entry, asset returns, and irrational exuberance: asset management anomalies in dry cargo shipping. Int J Shipp Transp Logist 5(6):652–667CrossRefGoogle Scholar
  9. Callaway MR, Esser JK (1984) Groupthink: effects of cohesiveness and problem-solving procedures on group decision making. Soc Behav Personal Int J 12(2):157–164CrossRefGoogle Scholar
  10. Chang D-Y (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95(3):649–655CrossRefGoogle Scholar
  11. Cochran WG (1977) Sampling techniques, 3rd edn. Wiley, New YorkGoogle Scholar
  12. Davey L (2017) If your team agrees on everything, working together is pointless. Harv Bus Rev, January 31, 2017 (online article)Google Scholar
  13. Dhar R, Simonson I (2003) The effect of forced choice on choice. J Mark Res 40(2):146–160CrossRefGoogle Scholar
  14. Duru O, Bulut E, Yoshida S (2012) Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: a cubic fuzzy-priority matrix design. Expert Syst Appl 39(5):4954–4964CrossRefGoogle Scholar
  15. Esser JK (1998) Alive and well after 25 years: a review of groupthink research. Organ Behav Hum Decis Process 73(2):116–141CrossRefGoogle Scholar
  16. Gilboa I (2010) Rational choice. MIT Press, Cambridge, USAGoogle Scholar
  17. Hanbin B, Nuanchen W (2010) Research on the selection of scale in AHP. In: Advanced computer theory and engineering (ICACTE), 2010 3rd international conference on, vol 6, pp 1–108Google Scholar
  18. Israely J (2009) The least known key economic indicator. TIME MagazineGoogle Scholar
  19. Ji P, Jiang R (2003) Scale transitivity in the AHP. J Oper Res Soc 54(8):896–905CrossRefGoogle Scholar
  20. Joshi R, Banwet DK, Shankar R (2011) A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain. Expert Syst Appl 38(8):10170–10182CrossRefGoogle Scholar
  21. Ka B (2011) Application of fuzzy AHP and ELECTRE to China dry port location selection. Asian J Shipp Logist 27(2):331–353CrossRefGoogle Scholar
  22. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291CrossRefGoogle Scholar
  23. Kaufmann A, Gupta MM (1991) Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New YorkGoogle Scholar
  24. Lee M, Pham H, Zhang X (1999) A methodology for priority setting with application to software development process. Eur J Oper Res 118:375–389CrossRefGoogle Scholar
  25. Levin IP, Schnittjer SK, Thee SL (1988) Information framing effects in social and personal decisions. J Exp Soc Psychol 24(6):520–529CrossRefGoogle Scholar
  26. Lootsma FA (1993) Scale sensitivity in the multiplicative AHP and SMART. J Multi-Criteria Decis Anal 2(2):87–110CrossRefGoogle Scholar
  27. Ma D, Zheng X (1991) 9/9-9/1 scale method of AHP. In: Proceedings of the second international symposium on the AHP, vol 1. University of Pittsburgh, Pittsburgh, pp 197–202Google Scholar
  28. Marks G, Miller N (1987) Ten years of research on the false-consensus effect: an empirical and theoretical review. Psychol Bull 102(1):72CrossRefGoogle Scholar
  29. McCauley C (1989) The nature of social influence in groupthink: compliance and internalization. J Pers Soc Psychol 57(2):250CrossRefGoogle Scholar
  30. Millet I, Saaty TL (2000) On the relativity of relative measures–accommodating both rank preservation and rank reversals in the AHP. Eur J Oper Res 121(1):205–212CrossRefGoogle Scholar
  31. Muchnik L, Aral S, Taylor SJ (2013) Social influence bias: a randomized experiment. Science 341(6146):647–651CrossRefGoogle Scholar
  32. Prasad AVS, Somasekhara N (1990) The analytic hierarchy process for choice of technologies: an application. Technol Forecast Soc Chang 38(2):151–158CrossRefGoogle Scholar
  33. Robbins L (1932) The nature and significance of economic science. Philos Econ Anthol 1:73–99Google Scholar
  34. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281CrossRefGoogle Scholar
  35. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
  36. Saaty TL (1987) Rank generation, preservation, and reversal in the analytic hierarchy decision process. Decis Sci 18(2):157–177CrossRefGoogle Scholar
  37. Saaty TL (2000) Fundamentals of decision making and priority theory with the analytic hierarchy process, vol 6. Rws Publications, PittsburghGoogle Scholar
  38. Saaty TL (2006) Rank from comparisons and from ratings in the analytic hierarchy/network processes. Eur J Oper Res 168(2):557–570CrossRefGoogle Scholar
  39. Sahin B, Senol YE (2015) A novel process model for marine accident analysis by using generic fuzzy-AHP algorithm. J Navig 68(1):162–183CrossRefGoogle Scholar
  40. Salo AA, Hämäläinen RP (1997) On the measurement of preferences in the analytic hierarchy process. J Multi-Criteria Decis Anal 6(6):309–319CrossRefGoogle Scholar
  41. Schenkerman S (1994) Avoiding rank reversal in AHP decision-support models. Eur J Oper Res 74(3):407–419CrossRefGoogle Scholar
  42. Scheufele DA, Tewksbury D (2007) Framing, agenda setting, and priming: the evolution of three media effects models. J Commun 57(1):9–20Google Scholar
  43. Şen CG, Şen S, Başlıgila H (2010) Pre-selection of suppliers through an integrated fuzzy analytic hierarchy process and max-min methodology. Int J Prod Res 48(6):1603–1625. Retrieved fromGoogle Scholar
  44. Simon HA (1982) Models of bounded rationality: empirically grounded economic reason, vol 3. MIT Press, Cambridge, USAGoogle Scholar
  45. Tadic D, Gumus AT, Arsovski S, Aleksic A, Stefanovic M (2013) An evaluation of quality goals by using fuzzy AHP and fuzzy TOPSIS methodology. J Intell Fuzzy Syst 25(3):547–556Google Scholar
  46. Tavana M, Kennedy DT, Rappaport J, Ugras YJ (1993) An AHP-Delphi group decision support system applied to conflict resolution in hiring decisions. J Manag Syst 5(1):49–74Google Scholar
  47. Thaler R (1985) Mental accounting and consumer choice. Mark Sci 4(3):199–214CrossRefGoogle Scholar
  48. UNCTAD (2013) Review of maritime transport. United Nations, New YorkGoogle Scholar
  49. van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(1–3):199–227Google Scholar
  50. Wang Y-M, Elhag TMS (2006) An approach to avoiding rank reversal in AHP. Decis Support Syst 42(3):1474–1480CrossRefGoogle Scholar
  51. Weck M, Klocke F, Schell H, Rüenauver E (1997) Evaluating alternative production cycles using the extended fuzzy AHP method. Eur J Oper Res 100:351–366CrossRefGoogle Scholar
  52. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Business AdministrationYildiz Technical UniversityIstanbulTurkey
  2. 2.School of Civil and Environmental EngineeringNanyang Technological UniversitySingaporeSingapore

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