Abstract
This paper presents an approach to improve the consistency of pair-wise comparison matrix in analytical hierarchy process (AHP) using teaching learning based optimization (TLBO) algorithm. The purpose of this proposed approach to minimize the consistency ratio (CR). Consistency test for the comparison matrix in AHP have been studied rigorously since AHP was introduced in 1970s. However, existing approaches are either too complicated or difficult. Most of them could not preserve the original judgments provided by an expert. To improve the consistency ratio (CR), this research work proposes a simple, effective and efficient method which will minimize the CR to almost zero while preserving the judgment values in pair-wise comparison matrix. The correctness of the proposed method is proved by applying it to two real world case studies reported in literature, namely new product design selection and material selection (work tool combination). The experimentation shows that the proposed approach is efficient and accurate to satisfy the consistency requirements of AHP.
Similar content being viewed by others
References
Arunachalam R, Mannan M (2000) Machinability of nickel-based high temperature alloys. Mach Sci Technol 4:127–168. doi:10.1080/10940340008945703
Besharati B, Azarm S, Kannan P (2006) A decision support system for product design selection: a generalized purchase modeling approach. Decis Support Syst 42:333–350. doi:10.1016/j.dss.2005.01.002
Borkar P, Sarode M, Malik L (2016) Acoustic signal based optimal route selection problem: performance comparison of multi-attribute decision making methods. KSII Trans Internet Inf Syst 10(2):647–669
Boubekri N, Rodriguez J, Asfour S (2003) Development of an aggregate indicator to assess the machinability of steels. J Mater Process Technol 134:159–165. doi:10.1016/s0924-0136(02)00446-6
Cao D, Leung L, Law J (2008) Modifying inconsistent comparison matrix in analytic hierarchy process: a heuristic approach. Decis Support Syst 44:944–953. doi:10.1016/j.dss.2007.11.002
Chakraborty P, Das S, Roy G, Abraham A (2011) On convergence of the multi-objective particle swarm optimizers. Inf Sci 181:1411–1425. doi:10.1016/j.ins.2010.11.036
Chen S, Hwang C (1992) Fuzzy multiple attribute decision making. Lect Notes Econ Math Syst. doi:10.1007/978-3-642-46768-4
Costa J (2011) A genetic algorithm to obtain consistency in analytic hierarchy process. BJOPM 8:55–64. doi:10.4322/bjopm.2011.003
Dong Y, Xu Y, Li H (2008) On consistency measures of linguistic preference relations. Eur J Oper Res 189:430–444. doi:10.1016/j.ejor.2007.06.013
Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge
Dravid SV, Utpat LS (2001) Machinability evaluation based on the surface finish criterion. J Inst Eng (India) Prod Eng Div 81:47–51
Efren MM, Mariana EMV, Rubi DCGR (2010) Differential evolution in constrained numerical optimization: an empirical study. Inf Sci 180:4223–4262
Enache S, Strjescu E, Opran C et al. (1995) Mathematical model for the establishment of the materials machinability. CIRP Ann Manuf Technol 44:79–82. doi:10.1016/s0007-8506(07)62279-3
Farmer J, Packard N, Perelson A (1986) The immune system, adaptation, and machine learning. Phys D 22:187–204. doi:10.1016/0167-2789(86)90240-x
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–70
Girsang A, Tsai C, Yang C (2014a) Ant algorithm for modifying an inconsistent pairwise weighting matrix in an analytic hierarchy process. Neural Comput Appl 26:313–327. doi:10.1007/s00521-014-1630-0
Girsang AS, Tsai CW, Yang CS (2014b) Ant colony optimization for reducing the consistency ratio in comparison matrix. In: Proceedings of the International Conference on Advances in Engineering and Technology (ICAET’14), pp 577–582
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Haque B, Belecheanu R, Barson R, Pawar K (2000) Towards the application of case based reasoning to decision-making in concurrent product development (concurrent engineering). Knowl Based Syst 13:101–112. doi:10.1016/s0950-7051(00)00051-4
Hsiao S, Chou J (2004) A creativity-based design process for innovative product design. Int J Ind Ergon 34:421–443. doi:10.1016/j.ergon.2004.05.005
Iida Y (2009) Ordinality consistency test about items and notation of a pairwise comparison matrix in AHP. In: Proceedings of the International Symposium on the Analytic Hierarchy Process
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06. Erciyes University
Karen A, Yildiz A, Kaya N et al (2006) Hybrid approach for genetic algorithm and Taguchi’s method based design optimization in the automotive industry. Int J Prod Res 44:4897–4914. doi:10.1080/00207540600619932
Keeney R, Raiffa H (1976) Decisions with multiple objectives; preferences and values tradeoffs. Wiley, New York
Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948
Kim K, Kang M, Kim J et al (2002) A study on the precision machinability of ball end milling by cutting speed optimization. J Mater Process Technol 130–131:357–362. doi:10.1016/s0924-0136(02)00824-5
Kulak O, Kahraman C (2005) Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. Int J Prod Econ 95:415–424. doi:10.1016/j.ijpe.2004.02.009
Li H, Ma L (2007) Detecting and adjusting ordinal and cardinal inconsistencies through a graphical and optimal approach in AHP models. Comput Oper Res 34:780–798. doi:10.1016/j.cor.2005.05.010
Lin C, Wang W, Yu W (2008) Improving AHP for construction with an adaptive AHP approach (A3). Autom Constr 17:180–187. doi:10.1016/j.autcon.2007.03.004
Lin M, Lee Y, Ho T (2011) Applying integrated DEA/AHP to evaluate the economic performance of local governments in China. Eur J Oper Res 209:129–140. doi:10.1016/j.ejor.2010.08.006
Liu J, Tang L (1999) A modified genetic algorithm for single machine scheduling. Comput Ind Eng 37:43–46. doi:10.1016/s0360-8352(99)00020-0
Lo C, Wang P, Chao K (2006) A fuzzy group-preferences analysis method for new-product development. Expert Syst Appl 31:826–834. doi:10.1016/j.eswa.2006.01.005
Maddulapalli A, Azarm S, Boyars A (2007) Sensitivity analysis for product design selection with an implicit value function. Eur J Oper Res 180:1245–1259. doi:10.1016/j.ejor.2006.03.055
Morehead M, Huang Y, Ted Hartwig K (2007) Machinability of ultrafine-grained copper using tungsten carbide and polycrystalline diamond tools. Int J Mach Tools Manuf 47:286–293. doi:10.1016/j.ijmachtools.2006.03.014
Ong S, Chew L (2000) Evaluating the manufacturability of machined parts and their setup plans. Int J Prod Res 38:2397–2415. doi:10.1080/00207540050031832
Ozer M (2005) Factors which influence decision making in new product evaluation. Eur J Oper Res 163:784–801. doi:10.1016/j.ejor.2003.11.002
Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67. doi:10.1109/mcs.2002.1004010
Peng Y, Kou G, Wang G et al (2011a) Ensemble of software defect predictors: an AHP-based evaluation method. Int J Inf Tech Decis Mak 10:187–206. doi:10.1142/s0219622011004282
Peng Y, Wang G, Kou G, Shi Y (2011b) An empirical study of classification algorithm evaluation for financial risk prediction. Appl Soft Comput 11:2906–2915. doi:10.1016/j.asoc.2010.11.028
Peng Y, Wang G, Wang H (2012) User preferences based software defect detection algorithms selection using MCDM. Inf Sci 191:3–13. doi:10.1016/j.ins.2010.04.019
Rao R (2005) Machinability evaluation of work materials using a combined multiple attribute decision making method. Int J Adv Manuf Technol 28:221–227
Rao R (2007) Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making. Springer series in advanced manufacturing
Rao R (2011) Advanced modeling and optimization of manufacturing processes. Springer series in advanced manufacturing. doi:10.1007/978-0-85729-015-1
Rao R (2013a) Decision making in manufacturing environment using graph theory and fuzzy multiple attribute decision making methods, vol 2. Springer series in advanced manufacturing
Rao R (2013b) Decision making in manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer series in advanced manufacturing. doi:10.1007/978-1-4471-4375-8
Rao R (2015) Teaching learning based optimization and its engineering applications. Springer, London
Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34
Rao R, Patel V (2012b) An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3:535–560. doi:10.5267/j.ijiec.2012.03.007
Rao R, Patel V (2013b) Multi-objective optimization of heat exchangers using a modified teaching–learning-based optimization algorithm. Appl Math Modell 37:1147–1162. doi:10.1016/j.apm.2012.03.043
Rao R, Patel V (2013c) Multi-objective optimization of two stage thermoelectric cooler using a modified teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26:430–445
Rao R, Savsani V, Vakharia D (2012a) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183:1–15. doi:10.1016/j.ins.2011.08.006
Saaty TL (2001) Deriving the AHP 1–9 scale from first principles. In: ISAHP 2001 Proceedings, Bern
Saaty TL (2003) Decision-making with the AHP: why is the principal eigenvector necessary. Eur J Oper Res 145(1):85–91
Saaty TL (2005) Theory and applications of the analytic network process: decision making with benefits, opportunities, costs and risks. RWS Publications, Pittsburgh (ISBN 1-888603-06-2)
Saaty TL (2006) The analytic network process, decision making with the analytic network process. Int Ser Oper Res Manag Sci 95:1–26
Šalak A, Vasilko K, Selecká M, Danninger H (2006) New short time face turning method for testing the machinability of PM steels. J Mater Process Technol 176:62–69
Shi W, Shen Q, Kong W, Ye B (2007) QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression. Eur J Med Chem 42:81–86
Suh NP (2001) Axiomatic design: advances and applications. Oxford University Press, New York
Yang I, Wang W, Yang T (2012) Automatic repair of inconsistent pairwise weighting matrices in analytic hierarchy process. Autom Constr 22:290–297. doi:10.1016/j.autcon.2011.09.004
Yildiz AR (2009) A novel hybrid immune algorithm for global optimization in design and manufacturing. Rob Comput Integr Manuf 25:261–270
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Borkar, P., Sarode, M.V. Modality of teaching learning based optimization algorithm to reduce the consistency ratio of the pair-wise comparison matrix in analytical hierarchy processing. Evolving Systems 9, 169–180 (2018). https://doi.org/10.1007/s12530-017-9185-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12530-017-9185-9