Abstract
The selection process of a suitable machine tool among the increased number of alternatives has been an important issue for manufacturing companies for years. This is because the improper selection of a machine tool may cause many problems that will affect the overall performance. In this paper, a decision support system (DSS) is presented to select the best alternative machine using a hybrid approach of fuzzy analytic hierarchy process (fuzzy AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE). A MATLAB- based fuzzy AHP is used to determine the weights of the criteria and it is called program for Priority Weights of the Evaluation Criteria (PWEC), and the PROMETHEE method is applied for the final ranking. The proposed model is structured to select the most suitable computer numerical controlled (CNC) turning centre machine for a flexible manufacturing cell (FMC) among the alternatives which are assigned from a database (DB) created for this purpose. A numerical example is presented to show the applicability of the model. It is concluded that the proposed model has the capability of dealing with a wide range of desired criteria and to select any type of machine tool required for building an FMC.
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References
Abdel-Malek L., Resare L. J. (2000) Algorithm based decision support system for the concerted selection of equipment in machining/assembly cells. International Journal of Production Research 38: 323–339
Albadvi A., Chaharsooghi S. K., Esfahanipour A. (2007) Decision making in stock trading: an application of PROMETHEE. European Journal of Operational Research 177: 673–683
Alberti, M., Ciurana, J., Rodriguez, C. A., & Ozel, T. (2009). Design of a decision support system for machine tool selection based on machine characteristics and performance tests. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0286-6.
Aly A. A., Subramaniam M. (1993) Design of an FMS decision support system. International Journal of Production Research 31: 2257–2273
Arslan M. C., Catay B., Budak E. (2004) A decision support system for machine tool selection. Journal of Manufacturing Technology Management 15: 101–109
Ayag Z. (2005) A fuzzy AHP based simulation approach to concept evaluation in a NPD environment. IIE Transaction 37: 827– 842
Ayag Z., Ozdemir R. G. (2006) A fuzzy AHP approach to evaluating machine tool alternatives. Journal of Intelligent Manufacturing 17: 179–190
Ayag Z. (2007) A hybrid approach to machine tool selection through AHP and simulation. International Journal of Production Research 45: 2029–2050
Ayag, Z., & Ozdemir, R. G. (2009). An intelligent approach to machine tool selection through fuzzy analytic network process. Journal of Intelligent Manufacturing. doi:10.1007/s10845-009-0269-7.
Behzadian M., Kazemzadeh R.B., Albadvi A., Aghdasi M. (2010) PROMETHEE: a comprehensive literature review on methodologies and applications. European Journal of Operational research 200: 198–215
Brans J. P., Vincke P. H. (1985) A preference ranking organization method: the PROMETHEE method for multiple criteria decision making. Management Science 31: 647–656
Cimren E., Catay B., Budak E. (2007) Development of machine tool selection system using AHP. International Journal of Advanced Manufacturing Technology 35: 363–376
Chan F. T. S., Abhary K. (1996) Design and evaluation of automated cellular manufacturing systems with simulation modeling and AHP approach: A case study. Integrated Manufacturing Systems 7: 39–52
Chan F. T. S., Jiang B., Tang N. K. H. (2000) The development of intelligent decision support tools to aid the design of flexible manufacturing systems. International Journal of Production Economics 65: 73–84
Chan F. T. S., Swarnkar R. (2006) Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and Computer-Integrated Manufacturing 22: 353–362
Chang, C.-W. (2010). Collaborative decision making algorithm for selection of optimal wire saw in photovoltaic wafer manufacture. Journal of Intelligent manufacturing. doi:10.1007/s10845-010-0391-6.
Chang T. H., Wang T. C. (2009) Using the fuzzy multicriteria decision making approach for measuring the possibility of successful knowledge management. Information Sciences 179: 355–370
Chtourou H., Masmoudi W., Maalej A. (2005) An expert system for manufacturing systems machine selection. Expert Systems with Applications 28: 461–467
Dagdevrin M. (2008) Decision making in equipment selection: An integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing 19: 397–406
Decision Lab (2000). Online, http://www.visualdecision.com.
Doosan Infracore Machine Tools (Online). http://www.infracorent.com.
Karsak E. E., Kuzgunkaya O. (2002) A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system. International Journal of Production Economics 79: 101–111
Keung K. W., IP W. H., Lee T. C. (2001) A genetic algorithm approach to the multiple machine tool selection problem. Journal of Intelligent Manufacturing 12: 331–342
Lee, A.R. (1995). Application of modified fuzzy AHP method to analyze bolting sequence on structural joints. PhD Dissertation, Lehigh University, A Bell & Howell Company, UMI Dissertation Services.
Li T. Sh., Huang H. H. (2009) Applying TRIZ and fuzzy AHP to develop innovative design for automated manufacturing systems. Expert Systems with Applications 36: 8302–8312
Lin Z. Ch., Yang Ch. B. (1996) Evaluation of machine selection by the AHP method. Journal of Materials Processing Technology 57: 253–258
Liu Sh. T. (2008) A fuzzy DEA/AR approach to the selection of flexible manufacturing systems. Computers and Industrial Engineering 54: 66–76
Mazak Machine Tools (Online). http://www.mazakusa.com.
Mishra S., Prakash , Tiwari M. K., Lashkari R. S. (2006) A fuzzy goal programming model of machine tool selection and operation allocation problem in FMS: a quick converging simulated-annealing based approach. International Journal of Production Research 44: 43–76
Moon C., Lee M., Seo Y., Lee Y. H. (2002) Integrated machine tool selection and operation sequencing with capacity and precedence constraints using genetic algorithm. Computers and Industrial Engineering 43: 605–621
Nakamura Tome- Methods Machine Tools Inc (Online). http://www.methodsmachine.com/machines/region/41/5.
Norrie, D. H., Fanvel, R., Gaines, B. R., & Mowchenko, M. (1989). A knowledge based decision support system for flexible manufacturing. In Proceedings of the 2nd international conference on industrial and engineering allocation of artificial intelligence and expert systems (Vol. 1, pp. 393–400).
Onut S., Kara S. S., Efendigil T. (2008) A hybrid fuzzy MCDM approach to machine tool selection election. Journal of Intelligent Manufacturing 19: 443–453
Rai R., Kameshwaran S., Tiwari M.K. (2002) Machine tool selection and operation allocation in FMS: solving a fuzzy goal programming model using a genetic algorithm. International Journal of Production Research 40: 641–665
Romi Machine Tools (Online). http://www.romiusa.com.
Saaty T. L. (1980) The analytic hierarchy process. McGraw-Hill, New York
Shang J., Sueyoshi T. (1995) A unified framework for the selection of a flexible manufacturing system. European Journal of Operational Research 85: 297–315
Stam A., Kuula M. (1991) Selecting a flexible manufacturing system using multiple criteria analysis. International Journal of Production Research 29: 803–820
Sun, B., Chen, H., Du, L., & Fang, Y. (2008) Machine tools selection technology for networked manufacturing. In Proceedings of the second international symposium on intelligent information technology application (Vol. 1, pp. 530–534)
Tang Y. C. (2009) An approach to budget allocation for an aerospace company – fuzzy analytic hierarchy process and artificial neural network. Neurocomputing 72: 3477–3489
Tansel I. Y., Yurdakul M. (2009) Development of a decision support system for machining centre selection. Expert Systems with applications 36: 3505–3513
Venkata Rao, R. (2007). Decision making in manufacturing environment, Machine selection in a flexible manufacturing cell (pp. 139–148). Springer Series in Advanced Manufacturing, Springer London.
Wang T. Y., Shaw Ch. F., Chen Y. L. (2000) Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision making approach. International Journal of Production Research 38: 2079–2097
Yurdakul M., Tansel I. Y. (2009) Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. Journal of Materials Processing Technology 209: 310–317
Zadeh L. A. (1965) Fuzzy sets. Information and Control 8: 338–353
Zadeh L.A. (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1: 3–28
Zadeh L. A. (1994) Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37: 37–44
Zahedi F. (1986) The analytic hierarchy process: A survey of the method and its applications. Interfaces 16(4): 96–108
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Taha, Z., Rostam, S. A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell. J Intell Manuf 23, 2137–2149 (2012). https://doi.org/10.1007/s10845-011-0560-2
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DOI: https://doi.org/10.1007/s10845-011-0560-2