Abstract
Modern world is a competitive world. To survive in this world, every industry must achieve competitiveness. So, it has become the most important task for them to select the best Advanced Manufacturing Technology (AMT). The process involves both quantitative and qualitative factors. The aim of this paper is to solve the problem by Fuzzy TOPSIS method. According to the method of TOPSIS, a closeness co-efficient is determined by calculating the distances to both the Fuzzy positive ideal solution (FPIS) and Fuzzy negative ideal solution (FNIS). Then, a Suitability Index (SI) is calculated by taking into account the Objective Factor Measurement (OFM) to rank the alternatives. Finally, a numerical example using triangular fuzzy numbers is shown to highlight the proposed method.
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Al-Ahmari, A.M.A.: Implementing CIM systems in SMEs. Int. J. Comput. Appl. Technol. 15, 122–127 (2002)
Al-Ahmari, A.M.A.: Evaluation of CIM technologies in Saudi industries using AHP. Int. J. Adv. Manuf. Technol. 34, 736–747 (2007)
Al-Ahmari, A.M.A.: A methodology for selection and evaluation of advanced manufacturing technologies. Int. J. Comput. Integr. Manuf. 21(7), 778–789 (2008)
Arbel, A., Seidmann, A.: Performance evaluation of PMS. IEEE Trans. Syst. Man Cybern. 14, 606–617 (1984)
Bellman, R.E., Zadeh, L.A.: Decision-making in a fuzzy environment. Manag. Sci. 17, 141–164 (1970)
Bojadziev, G., Bojadziev, M.: Fuzzy sets, fuzzy logic, applications. In: Advances in fuzzy systems-applications and theory vol. 5. World Scientific, Singapore (1995)
Buyukozkan, G., Feyzioglu, O., Nebol, E.: Selection of the strategic alliance partner in logistics value chain. Int. J. Prod. Econ. 113, 148–158 (2008)
Celik, M., Kahraman, C., Cebi, S., Er, I.D.: Fuzzy axiomatic design-based performance evaluation model for docking facilities in shipbuilding industry:the case of Turkish shipyards. Exp. Syst. Appl. (2007). doi:10.1016/j.eswa,2007.09.055
Chamodrakas, I., Batis, D., Martakos, D.: Supplier selection in electronic market places using satisficing and fuzzy AHP. Exp. Syst. Appl. 37, 490–498 (2010)
Chiadamrong, N.: An integrated fuzzy multi criteria decision making method for the manufacturing strategies selection. Comput. Ind. Eng. 37, 433–436 (1999)
Chuu, S.-J.: Group decision-making model using fuzzy multiple attributes analysis for the evaluation of advanced manufacturing technology. Fuzzy Sets Syst. 160, 586–602 (2009)
Chuu, S.-J.: Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Comput. Ind. Eng. 57, 1033–1042 (2009)
Datta, V., Sambasivarao, K.V., Kodali, R., Deshmukh, S.G.: Multi-attribute decision model using the analytic hierarchy process for the justification of manufacturing systems. Int. J. Prod. Econ. 28, 227–234 (1992)
Demmel, J.G., Askin, R.G.: A multiple-objective decision model for the evaluation of advanced manufacturing system technologies. J. Manuf. Syst. 11(3), 179–194 (1992)
Hung, K.-C., Julian, P., Chien, T., Jin, W.T.-H.: A decision support system for engineering design based on an enhanced fuzzy MCDM approach. Exp. Syst. Appl. 37, 202–213 (2010)
Hwang, C.-L., Yoon, K.P.: Multiple Attribute Decision Making: Methods and applications. Springer, Berlin (1981)
Kahraman, C., Cebi, S.: Anew multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Exp. Syst. Appl. 36, 4848–4861 (2009)
Kahraman, C., Ulukan, Z.: Fuzzy multi-objective linear programming based justification of advanced manufacturing systems. IEEE. 226–232 (1996)
Kahraman, C., Ruan, D., Dogan, I.: Fuzzy group decision making for facility location selection. Inform. Sci. 157, 135–153 (2003)
Karsak, E.E.: Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. Int. J. Prod. Res. 40(13), 3167–3181 (2002)
Karsak, E.E., Tolga, E.: Fuzzy multi-criteria decision making procedure for evaluating advanced manufacturing system investments. Int. J. Prod. Econ. 69, 49–64 (2001)
Kassicieh, S.K., Ravinder, H.V., Yourstone, S.A.: Proposed design of a DSS for the justification of advanced manufacturing technologies. IEEE Trans. Eng. Manag. 404, 398–402 (1993)
Kengpol, A., O’Brien, C.: The development of a decision support tool for the selection of advanced technology to achieve rapid product development. Int. J. Prod. Econ. 69, 177–191 (2001)
Liang, G.S., Wang, M.J.J.: A fuzzy multi-criteria decision-making approach for robot selection. Robot. Comput. Integr. Manuf. 10, 267–274 (1993)
Luong Lee, H.S.: A decision support system for the selection of computer integrated manufacturing technologies. Robot. Comput. Integr. Manuf. 14, 45–53 (1998)
Maldonado, A., Garcia, J.L., Alvarado, A., Balderrama, C.O.: A hierarchical fuzzy axiomatic design methodology for ergonomic compatibility evaluation of advanced manufacturing technology. Int. J. Adv. Manuf. Technol. (2012). doi:10.1007/s00170-012-4316-8
Meredith, J.R., Suresh, N.C.: Justification techniques for advanced manufacturing technologies. Int. J. Prod. Res. 24, 1043–1057 (1986)
Miltenburg, G.J., Krinsky, I.: Evaluating flexible manufacturing systems. IIE Trans. 19, 222–233 (1987)
Mohanty, R.P., Deshmukh, S.G.: Advanced manufacturing technology selection: a strategic model for learning and evaluation. Int. J. Prod. Econ. 55, 295–307 (1998)
Mohanty, R.P., Veokataraman, S.: Use of the analytic hierarchy process for selecting automated manufacturing systems. Int. J. Oper. Prod. Manage 13, 45–57 (1993)
Nagarur, N.: Some performance measures of flexible manufacturing systems. Int. J. Prod. Res. 30, 799–809 (1992)
Nelson, C.A.: A scoring model for flexible manufacturing systems project selection. Eur. J. Oper. Res. 24, 346–359 (1986)
O’Kane, J.F., Spenceley, J.R., Taylor, R.: Simulation as an essential tool for advanced manufacturing technology problems. J. Mater. Process. Technol. 107, 412–424 (2000)
Orddobadi, S.M., Nancy, J.: Development of a justification tool for advanced manufacturing technologies: (SWBVA). J. Eng. Technol. Manag. 18, 157–184 (2001)
Park, C.S., Kim, G.T.: An economic evaluation model for advanced manufacturing systems using activity based costing. J. Manuf. Syst. 16, 439–451 (1995)
Perego, A., Rangone, A.: A reference framework for the application of MADM fuzzy techniques to selecting AMTS. Int. J. Prod. Res. 36, 437–458 (1998)
Rouse, W.B.: intelligent decision support for advanced manufacturing systems. Am. Soc. Mech. Eng. (1988)
Sambasivarao, V., Deshmukh, S.G.: A decision support system for selection and justification of advanced manufacturing technologies. Prod. Plan. Control 8, 270–284 (1997)
Samll, M.H., Chen, I.: Economic and strategic justification of AMT inference from industrial practice. Int. J. Prod. Econ. 49, 65–75 (1997)
Stam, A., Kuula, M.: Selecting a flexible manufacturing system using multiple criteria analysis. Int. J. Prod. Res. 29, 803–820 (1991)
Talluri, S., Yoon, K.P.: A cone-ratio DEA approach for AMT justification. Int. J. Prod. Econ. 66, 119–129 (2000)
Wabalickis, R.N.: Justification of FMS with the analytic hierarchy process. J. Manuf. Syst. 7, 175–182 (1988)
Yurdakul, M.: Selection of computer-integrated manufacturing technologies using a combined analysis hierarchy process and goal programming model. Robot. Comput. Integr. Manuf. 20, 329–340 (2004)
Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8, 199–249 (1975)
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The authors acknowledge the support of Jadavpur University, Kolkata, India in carrying out this work.
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Nath, S., Sarkar, B. Decision system framework for performance evaluation of advanced manufacturing technology under fuzzy environment. OPSEARCH 55, 703–720 (2018). https://doi.org/10.1007/s12597-016-0262-9
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DOI: https://doi.org/10.1007/s12597-016-0262-9