Analytical hierarchy process method and data envelopment analysis application in terrain vehicle selection
Abstract
Selection of a terrain vehicle for performing different tasks is an important factor, which influences the mobility of a user through the quality of conducting transport activities. This paper is dealing with the problem of the terrain vehicle selection for the equipping of military units which, are to be engaged in multinational operations, using the Analytical Hierarchy Process (AHP) method and Data Envelopment Analysis (DEA). Determination of the relative importance of criteria, which are used for evaluation of potential alternatives is conducted through AHP method. The results proposed by the AHP method are used as multiple outputs of the defined DEA model for the selection of the terrain vehicle. Based on the DEA model the efficiencies of alternatives are defined and also the final ranking of alternatives is determined. Besides the hybrid model AHP-DEA, which is the integral part of a basic multicriteria model in this paper the possible applications of Best Worst Method (BWM) and FUll COnsistency Model (FUCOM) are presented through validation of models. The validation is conducted through statistical data obtained by application of different multicriteria techniques, using Spearman’s Correlation Coefficient (SCC).
Keyword : terrain vehicle, selection, alternative, multicriteria selection, AHP, DEA, BWM, FUCOM
How to Cite
Starčević, S., Bojović, N., Junevičius, R., & Skrickij, V. (2019). Analytical hierarchy process method and data envelopment analysis application in terrain vehicle selection. Transport, 34(5), 600-616. https://doi.org/10.3846/transport.2019.11710
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Banker, R. D.; Charnes, A.; Cooper, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30(9): 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Bojanic, D.; Kovač, M.; Bojanic, M.; Ristic, V. 2018. Multi-criteria decision-making in A defensive operation of the guided anti-tank missile battery: an example of the hybrid model fuzzy AHP – MABAC, Decision Making: Applications in Management and Engineering 1(1): 51–66.
Božanić, D.; Tešić, D.; Milićević, J. 2018. A hybrid fuzzy AHP-MABAC model: application in the Serbian army – the selection of the location for deep wading as a technique of crossing the river by tanks, Decision Making: Applications in Management and Engineering 1(1): 143–164.
Byun, D.-H. 2001. The AHP approach for selecting an automobile purchase model, Information & Management 38(5): 289–297. https://doi.org/10.1016/s0378-7206(00)00071-9
Charnes, A.; Clark, C. T.; Cooper, W. W.; Golany, B. 1984. A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces, Annals of Operations Research 2(1): 95–112. https://doi.org/10.1007/BF01874734
Charnes, A.; Cooper, W. W.; Dieck-Assad, M.; Golany, B.; Wiggins, D. E. 1985. Efficiency Analysis of Medical Care Resources in the U.S. Army Health Service Command, Research Report CCS 516, Center for Cybernetic Studies, University of Texas at Austin, TX, US. 25 p.
Charnes, A.; Cooper, W. W.; Rhodes, E. 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2(6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chatterjee, P.; Mondal, S.; Boral, S.; Banerjee, A.; Chakraborty, S. 2017. A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attributive border approximation method, Facta Universitatis, Series: Mechanical Engineering 15(3): 439–456. https://doi.org/10.22190/FUME170508024C
Crary, M.; Nozick, L. K.; Whitaker, L. R. 2002. Sizing the US destroyer fleet, European Journal of Operational Research 136(3): 680–695. https://doi.org/10.1016/S0377-2217(01)00031-5
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Emrouznejad, A.; Parker, B. R.; Tavares, G. 2008. Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences 42(3): 151–157. https://doi.org/10.1016/j.seps.2007.07.002
Fatimah, S.; Mahmudah, U. 2017. Two-stage data envelopment analysis (DEA) for measuring the efficiency of elementary schools in Indonesia, International Journal of Environmental & Science Education 12(8): 1971–1987.
Giacomini, C.; Longo, G.; Lunardi, A.; Padoano, E. 2016. AHP‐aided evaluation of logistic and transport solutions in a seaport, in F. De Felice, A. Petrillo, T. Saaty (Eds.). Applications and Theory of Analytic Hierarchy Process: Decision Making for Strategic Decisions, 115–141. https://doi.org/10.5772/63686
Gigović, L.; Pamučar, D.; Bajić, Z.; Milićević, M. 2016. The combination of expert judgment and GIS–MAIRCA analysis for the selection of sites for ammunition depots, Sustainability 8(4): 372. https://doi.org/10.3390/su8040372
Ignaccolo, M.; Inturri, G.; García-Melón, M.; Giuffrida, N.; Le Pira, M.; Torrisi, V. 2017. Combining analytic hierarchy process (AHP) with role-playing games for stakeholder engagement in complex transport decisions, Transportation Research Procedia 27: 500–507. https://doi.org/10.1016/j.trpro.2017.12.069
Ilić, I.; Petrevska, I. 2018. Using DEA method for determining tourism efficiency of Serbia and the surrounding countries, Hotel and Tourism Management 6(1): 73–80. https://doi.org/10.5937/menhottur1801073I
Ishizaka, A.; Labib, A. 2009. Analytic hierarchy process and expert choice: benefits and limitations, OR Insight 22(4): 201–220. https://doi.org/10.1057/ori.2009.10
Lindebo, E. 2004. Trends in the economic capacity of the Danish fishing fleet, 1996–2002, Acta Agriculturae Scandinavica, Section C – Food Economics 1(4): 207–221. https://doi.org/10.1080/16507540410019719
Lukovac, V.; Pamučar, D.; Popović, M.; Đorović, B. 2017. Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm, Expert Systems with Applications 90: 318–331. https://doi.org/10.1016/j.eswa.2017.08.034
Managi, S.; Karemera, D. 2004. Input and output biased technological change in US agriculture, Applied Economics Letters 11(5): 283–286. https://doi.org/10.1080/1350485042000221526
Minutolo, M. C. 2003. Use of analytic hierarchy process modeling in the military decision making process for course of action evaluation and unit cohesion, in The 7th International Symposium on the Analytic Hierarchy Process (ISAHP 2003), 7–9 August 2003, Bali, Indonesia, 349–358. Available from Internet: http://www.isahp.org/2003Proceedings/paper/p39.pdf
Mukhametzyanov, I.; Pamučar, D. 2018. A sensitivity analysis in MCDM problems: a statistical approach, Decision Making: Applications in Management and Engineering 1(2): 51–80.
Nunić, Z. 2018. Evaluation and selection of manufacturer PVC carpentry using FUCOM-MABAC model, Operational Research in Engineering Sciences: Theory and Applications 1(1): 13–28.
Olivková, I. 2017. Methodology for assessment of electronic payment systems in transport using AHP method, Lecture Notes in Networks and Systems 36: 290–299. https://doi.org/10.1007/978-3-319-74454-4_28
Pamučar, D.; Badi, I.; Korica, S., Obradović, R. 2018a. A novel approach for the selection of power-generation technology using a linguistic neutrosophic CODAS method: a case study in Libya, Energies 11(9): 2489. https://doi.org/10.3390/en11092489
Pamučar, D.; Lukovac, V.; Božanić, D.; Komazec, N. 2018b. Multi- criteria FUCOM-MAIRCA model for the evaluation of level crossings: case study in the Republic of Serbia, Operational Research in Engineering Sciences: Theory and Applications 1(1): 108–129.
Pamučar, D.; Stević, Ž.; Sremac, S. 2018c. A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM), Symmetry 10(9): 393. https://doi.org/10.3390/sym10090393
Pamučar, D.; Stević, Ž.; Zavadskas, E. K. 2018d. Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages, Applied Soft Computing 67: 141–163. https://doi.org/10.1016/j.asoc.2018.02.057
Popović, M.; Kuzmanović, M.; Savić, G. 2018. A comparative empirical study of analytic hierarchy process and conjoint analysis: literature review, Decision Making: Applications in Management and Engineering 1(2): 153–163.
Ramanathan, R. 2006. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process, Computers & Operations Research 33(5): 1289–1307. https://doi.org/10.1016/j.cor.2004.09.020
Raymundo, H.; Reis, J. G. M. 2017. Passenger transport drawbacks: an analysis of its “disutilities” applying the AHP approach in a case study in Tokyo, Japan, IFIP Advances in Information and Communication Technology 513: 545–552. https://doi.org/10.1007/978-3-319-66923-6_64
Rezaei, J. 2015. Best-worst multi-criteria decision-making method, Omega 53: 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Saaty, T. L. 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill. 287 p.
Stefanović-Marinović, J.; Troha, S.; Milovančević, M. 2017. An application of multicriteria optimization to the two-carrier two-speed planetary gear trains, Facta Universitatis, Series: Mechanical Engineering 15(1): 85–95. https://doi.org/10.22190/FUME160307002S
Steuer, R. E.; Na, P. 2003. Multiple criteria decision making combined with finance: a categorized bibliographic study, European Journal of Operational Research 150(3): 496–515. https://doi.org/10.1016/S0377-2217(02)00774-9
Stević, Ž.; Pamučar, D.; Vasiljević, M.; Stojić, G.; Korica, S. 2017. Novel integrated multi-criteria model for supplier selection: case study construction company, Symmetry 9(11): 279. https://doi.org/10.3390/sym9110279
Stojić, G.; Stević, Ž.; Antuchevičienė, J.; Pamučar, D.; Vasiljević, M. 2018. A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products, Information 9(5): 121. https://doi.org/10.3390/info9050121
Vaidya, O. S.; Kumar, S. 2006. Analytic hierarchy process: an overview of applications, European Journal of Operational Research 169(1): 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Wang, Y.-M.; Liu, J.; Elhag, T. M. S. 2008. An integrated AHP–DEA methodology for bridge risk assessment, Computers & Industrial Engineering 54(3): 513–525. https://doi.org/10.1016/j.cie.2007.09.002
Yang, Z. 2006. A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies, Mathematical and Computer Modelling 43(7–8): 910–919. https://doi.org/10.1016/j.mcm.2005.12.011
Zavras, A. I.; Tsakos, G.; Economou, C.; Kyriopoulos. J. 2002. Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network, Journal of Medical Systems 26(4): 285–292. https://doi.org/10.1023/A:1015860318972
Zinaja, D.; Arsić, M. 2011. Saobraćajna služba u obezbeđenju pokretljivosti vojske Srbije, Vojno delo (3): 258–283. (in Serbian).
Aghdaie, S. F. A.; Yousefi, E. 2011. The comparative analysis of affecting factors on purchasing domestic and imported cars in Iran market – using AHP technique, International Journal of Marketing Studies 3(2): 142–150. https://doi.org/10.5539/ijms.v3n2p142
Apak, S.; Göğüş, G. G.; Karakadılar, İ. S. 2012. An analytic hierarchy process approach with a novel framework for luxury car selection, Procedia – Social and Behavioral Sciences 58: 1301–1308. https://doi.org/10.1016/j.sbspro.2012.09.1113
Aull-Hyde, R.; Davis, K. A. 2012. Military applications of the analytic hierarchy process, International Journal of Multicriteria Decision Making 2(3): 267–281. https://doi.org/10.1504/IJMCDM.2012.047847
Bahadori, M.; Ravangard, R.; Yaghoubi, M.; Alimohammadzadeh, K. 2014. Assessing the service quality of Iran military hospitals: joint commission international standards and analytic hierarchy process (AHP) technique, Journal of Education and Health Promotion 3: 98.
Banker, R. D.; Charnes, A.; Cooper, W. W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science 30(9): 1078–1092. https://doi.org/10.1287/mnsc.30.9.1078
Bojanic, D.; Kovač, M.; Bojanic, M.; Ristic, V. 2018. Multi-criteria decision-making in A defensive operation of the guided anti-tank missile battery: an example of the hybrid model fuzzy AHP – MABAC, Decision Making: Applications in Management and Engineering 1(1): 51–66.
Božanić, D.; Tešić, D.; Milićević, J. 2018. A hybrid fuzzy AHP-MABAC model: application in the Serbian army – the selection of the location for deep wading as a technique of crossing the river by tanks, Decision Making: Applications in Management and Engineering 1(1): 143–164.
Byun, D.-H. 2001. The AHP approach for selecting an automobile purchase model, Information & Management 38(5): 289–297. https://doi.org/10.1016/s0378-7206(00)00071-9
Charnes, A.; Clark, C. T.; Cooper, W. W.; Golany, B. 1984. A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces, Annals of Operations Research 2(1): 95–112. https://doi.org/10.1007/BF01874734
Charnes, A.; Cooper, W. W.; Dieck-Assad, M.; Golany, B.; Wiggins, D. E. 1985. Efficiency Analysis of Medical Care Resources in the U.S. Army Health Service Command, Research Report CCS 516, Center for Cybernetic Studies, University of Texas at Austin, TX, US. 25 p.
Charnes, A.; Cooper, W. W.; Rhodes, E. 1978. Measuring the efficiency of decision making units, European Journal of Operational Research 2(6): 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Chatterjee, P.; Mondal, S.; Boral, S.; Banerjee, A.; Chakraborty, S. 2017. A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attributive border approximation method, Facta Universitatis, Series: Mechanical Engineering 15(3): 439–456. https://doi.org/10.22190/FUME170508024C
Crary, M.; Nozick, L. K.; Whitaker, L. R. 2002. Sizing the US destroyer fleet, European Journal of Operational Research 136(3): 680–695. https://doi.org/10.1016/S0377-2217(01)00031-5
Dağdeviren, M.; Yavuz, S.; Kılınç, N. 2009. Weapon selection using the AHP and TOPSIS methods under fuzzy environment, Expert Systems with Applications 36(4): 8143–8151. https://doi.org/10.1016/j.eswa.2008.10.016
Emrouznejad, A.; Parker, B. R.; Tavares, G. 2008. Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA, Socio-Economic Planning Sciences 42(3): 151–157. https://doi.org/10.1016/j.seps.2007.07.002
Fatimah, S.; Mahmudah, U. 2017. Two-stage data envelopment analysis (DEA) for measuring the efficiency of elementary schools in Indonesia, International Journal of Environmental & Science Education 12(8): 1971–1987.
Giacomini, C.; Longo, G.; Lunardi, A.; Padoano, E. 2016. AHP‐aided evaluation of logistic and transport solutions in a seaport, in F. De Felice, A. Petrillo, T. Saaty (Eds.). Applications and Theory of Analytic Hierarchy Process: Decision Making for Strategic Decisions, 115–141. https://doi.org/10.5772/63686
Gigović, L.; Pamučar, D.; Bajić, Z.; Milićević, M. 2016. The combination of expert judgment and GIS–MAIRCA analysis for the selection of sites for ammunition depots, Sustainability 8(4): 372. https://doi.org/10.3390/su8040372
Ignaccolo, M.; Inturri, G.; García-Melón, M.; Giuffrida, N.; Le Pira, M.; Torrisi, V. 2017. Combining analytic hierarchy process (AHP) with role-playing games for stakeholder engagement in complex transport decisions, Transportation Research Procedia 27: 500–507. https://doi.org/10.1016/j.trpro.2017.12.069
Ilić, I.; Petrevska, I. 2018. Using DEA method for determining tourism efficiency of Serbia and the surrounding countries, Hotel and Tourism Management 6(1): 73–80. https://doi.org/10.5937/menhottur1801073I
Ishizaka, A.; Labib, A. 2009. Analytic hierarchy process and expert choice: benefits and limitations, OR Insight 22(4): 201–220. https://doi.org/10.1057/ori.2009.10
Lindebo, E. 2004. Trends in the economic capacity of the Danish fishing fleet, 1996–2002, Acta Agriculturae Scandinavica, Section C – Food Economics 1(4): 207–221. https://doi.org/10.1080/16507540410019719
Lukovac, V.; Pamučar, D.; Popović, M.; Đorović, B. 2017. Portfolio model for analyzing human resources: An approach based on neuro-fuzzy modeling and the simulated annealing algorithm, Expert Systems with Applications 90: 318–331. https://doi.org/10.1016/j.eswa.2017.08.034
Managi, S.; Karemera, D. 2004. Input and output biased technological change in US agriculture, Applied Economics Letters 11(5): 283–286. https://doi.org/10.1080/1350485042000221526
Minutolo, M. C. 2003. Use of analytic hierarchy process modeling in the military decision making process for course of action evaluation and unit cohesion, in The 7th International Symposium on the Analytic Hierarchy Process (ISAHP 2003), 7–9 August 2003, Bali, Indonesia, 349–358. Available from Internet: http://www.isahp.org/2003Proceedings/paper/p39.pdf
Mukhametzyanov, I.; Pamučar, D. 2018. A sensitivity analysis in MCDM problems: a statistical approach, Decision Making: Applications in Management and Engineering 1(2): 51–80.
Nunić, Z. 2018. Evaluation and selection of manufacturer PVC carpentry using FUCOM-MABAC model, Operational Research in Engineering Sciences: Theory and Applications 1(1): 13–28.
Olivková, I. 2017. Methodology for assessment of electronic payment systems in transport using AHP method, Lecture Notes in Networks and Systems 36: 290–299. https://doi.org/10.1007/978-3-319-74454-4_28
Pamučar, D.; Badi, I.; Korica, S., Obradović, R. 2018a. A novel approach for the selection of power-generation technology using a linguistic neutrosophic CODAS method: a case study in Libya, Energies 11(9): 2489. https://doi.org/10.3390/en11092489
Pamučar, D.; Lukovac, V.; Božanić, D.; Komazec, N. 2018b. Multi- criteria FUCOM-MAIRCA model for the evaluation of level crossings: case study in the Republic of Serbia, Operational Research in Engineering Sciences: Theory and Applications 1(1): 108–129.
Pamučar, D.; Stević, Ž.; Sremac, S. 2018c. A new model for determining weight coefficients of criteria in MCDM models: full consistency method (FUCOM), Symmetry 10(9): 393. https://doi.org/10.3390/sym10090393
Pamučar, D.; Stević, Ž.; Zavadskas, E. K. 2018d. Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages, Applied Soft Computing 67: 141–163. https://doi.org/10.1016/j.asoc.2018.02.057
Popović, M.; Kuzmanović, M.; Savić, G. 2018. A comparative empirical study of analytic hierarchy process and conjoint analysis: literature review, Decision Making: Applications in Management and Engineering 1(2): 153–163.
Ramanathan, R. 2006. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process, Computers & Operations Research 33(5): 1289–1307. https://doi.org/10.1016/j.cor.2004.09.020
Raymundo, H.; Reis, J. G. M. 2017. Passenger transport drawbacks: an analysis of its “disutilities” applying the AHP approach in a case study in Tokyo, Japan, IFIP Advances in Information and Communication Technology 513: 545–552. https://doi.org/10.1007/978-3-319-66923-6_64
Rezaei, J. 2015. Best-worst multi-criteria decision-making method, Omega 53: 49–57. https://doi.org/10.1016/j.omega.2014.11.009
Saaty, T. L. 1980. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill. 287 p.
Stefanović-Marinović, J.; Troha, S.; Milovančević, M. 2017. An application of multicriteria optimization to the two-carrier two-speed planetary gear trains, Facta Universitatis, Series: Mechanical Engineering 15(1): 85–95. https://doi.org/10.22190/FUME160307002S
Steuer, R. E.; Na, P. 2003. Multiple criteria decision making combined with finance: a categorized bibliographic study, European Journal of Operational Research 150(3): 496–515. https://doi.org/10.1016/S0377-2217(02)00774-9
Stević, Ž.; Pamučar, D.; Vasiljević, M.; Stojić, G.; Korica, S. 2017. Novel integrated multi-criteria model for supplier selection: case study construction company, Symmetry 9(11): 279. https://doi.org/10.3390/sym9110279
Stojić, G.; Stević, Ž.; Antuchevičienė, J.; Pamučar, D.; Vasiljević, M. 2018. A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products, Information 9(5): 121. https://doi.org/10.3390/info9050121
Vaidya, O. S.; Kumar, S. 2006. Analytic hierarchy process: an overview of applications, European Journal of Operational Research 169(1): 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
Wang, Y.-M.; Liu, J.; Elhag, T. M. S. 2008. An integrated AHP–DEA methodology for bridge risk assessment, Computers & Industrial Engineering 54(3): 513–525. https://doi.org/10.1016/j.cie.2007.09.002
Yang, Z. 2006. A two-stage DEA model to evaluate the overall performance of Canadian life and health insurance companies, Mathematical and Computer Modelling 43(7–8): 910–919. https://doi.org/10.1016/j.mcm.2005.12.011
Zavras, A. I.; Tsakos, G.; Economou, C.; Kyriopoulos. J. 2002. Using DEA to evaluate efficiency and formulate policy within a Greek national primary health care network, Journal of Medical Systems 26(4): 285–292. https://doi.org/10.1023/A:1015860318972
Zinaja, D.; Arsić, M. 2011. Saobraćajna služba u obezbeđenju pokretljivosti vojske Srbije, Vojno delo (3): 258–283. (in Serbian).