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Integration of graph theory and matrix approach with fuzzy AHP for equipment selection

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2013

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Purpose: The main purpose of this paper is proposing a new integrated method to equipment selection. Proposed approach is based on fuzzy Analytic Hierarchy Process (FAHP) and GTMA (graph theory and matrix approach) methods that are used for equipment selection. Design/methodology/approach: In this paper, a two-step fuzzy-AHP and GTMA methodology is structured here that GTMA uses fuzzy-AHP result weights as input weights. Then a real case study is presented to show applicability and performance of the methodology. It can be said that using linguistic variables makes the evaluation process more realistic. Because evaluation is not an exact process and has fuzziness in its body. Here, the usage of fuzzy-AHP weights in GTMA makes the application more realistic and reliable. Proposed approach is applied to a problem of selecting CNC machines to be purchased in a company. Findings: The outcome of this research is ranking and selecting equipment based on Fuzzy AHP and GTMA techniques. According to this method, the first CNC machine (CNC1) is the best machine among other machines. Originality/value: This paper offers a new integrated method for equipment selection that can be used in other areas such as supplier selection, facility location selection and etc.
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  • English

REFERENCES

References: 

Atmani, A., & Lashkari, R.S. (1998). A model of machine tool selection and operation allocation
in flexible manufacturing system. International Journal of Production Research, 36,
1339-1349. http://dx.doi.org/10.1080/002075498193354
Ayag, Z., & Ozdemir, R.G. (2006). A fuzzy AHP approach to evaluating machine tool
alternatives. Journal of Intelligent Manufacturing, 17, 179-190. http://dx.doi.org/10.1007/s10845-
005-6635-1
Beaulieu, A., Gharbi, A., & Kadi, A. (1997). An algorithm for the cell formation and the machine
selection problems in the design of a cellular manufacturing system. International Journal of
Production Research, 35, 1857-1874. http://dx.doi.org/10.1080/002075497194958
Buckley, J.J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17, 233-247.
http://dx.doi.org/10.1016/0165-0114(85)90090-9
Chang, D.Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal
of Operational Research, 95, 649-655. http://dx.doi.org/10.1016/0377-2217(95)00300-2
Chen, M.A. (1999). Heuristic for solving manufacturing process and equipment selection
problems. International Journal of Production Research, 37, 359-374.
http://dx.doi.org/10.1080/002075499191814
Darvish, M., Yasaei, M. & Saeedi, A.(2009). Application of the graph theory and matrix
methods to contractor ranking. International Journal of Project Management, 27(6), 610-619.
http://dx.doi.org/10.1016/j.ijproman.2008.10.004
Dellurgio, S.A., Foster, S.T., & Dickerson, G. (1997). Utilizing simulation to develop economic
equipment selection and sampling plans for integrated circuit manufacturing. International
Journal of Production Research, 35, 137-155. http://dx.doi.org/10.1080/002075497196028
Faisal, M.N., Banwet, D. & Shankar, R. (2007). Quantification of risk mitigation environment of
supply chains using graph theory and matrix methods. European Journal of Industrial
Engineering, 1(1), 22-39. http://dx.doi.org/10.1504/EJIE.2007.012652
Kahraman, C., Cebeci, U., & Ulukan, Z. (2003). Multi-criteria supplier selection using fuzzy
AHP. Logistics Information Management, 16(6), 382-394.
http://dx.doi.org/10.1108/09576050310503367
Karsak, E.E. (2002). Distance-based fuzzy MCDM approach for evaluating flexible
manufacturing system alternatives. International Journal of Production Research, 40(13),
3167-3181. http://dx.doi.org/10.1080/00207540210146062
Kaufmann, A., & Gupta, M.M. (1988). Fuzzy mathematical models in engineering and
management science. Amsterdam: North-Holland.
Kulak, O., Durmusoglu, M.B., & Kahraman, C. (2005). Fuzzy multi attribute equipment
selection based on information axiom. Journal of Materials Processing Technology, 169,
337-345. http://dx.doi.org/10.1016/j.jmatprotec.2005.03.030
Li, S., Wang, H., Hu, S., Lin, Y., & Abell, J. (2011). Automatic generation of assembly system
configuration with equipment selection for automotive battery manufacturing. Journal of
Manufacturing Systems, 30, 188-195. http://dx.doi.org/10.1016/j.jmsy.2011.07.009
Nourani, Y., & Andresen, B. (1999). Exploration of NP-hard enumeration problems by simulated
annealing -the spectrum values of permanents. Theoretical computer science, 215(1-2),
51-68. http://dx.doi.org/10.1016/S0304-3975(99)80002-4
Oeltjenbruns, H., Kolarik, W.J., & Kirschner, R.S. (1995).Strategic planning in manufacturing
Systems-AHP application to an equipment replacement decision. International Journal of
Production Economics, 38, 189-197. http://dx.doi.org/10.1016/0925-5273(94)00092-O
Rao, R.V. (2007). Decision making in the manufacturing environment: using graph theory and
fuzzy multiple attribute decision making methods. London: Springer.
Rao, R.V. (2006). A decision-making framework model for evaluating flexible manufacturing
systems using digraph and matrix methods. The International Journal of Advanced
Manufacturing Technology, 30(11), 1101-1110. http://dx.doi.org/10.1007/s00170-005-0150-6
Saaty, T.L. (1980). The analytic hierarchy process. New York: McGraw- Hill.
Safari, H., Fathi, M.R., & Faghih, A. (2011). Applying fuzzy AHP and fuzzy TOPSIS to Machine
Selection. Journal of American Science, 7(9), 755-765.
Standing, G., Flores, B., & Olson, D. (2001).Understanding managerial preferences in selection
equipment. Journal of Operation Management, 19, 23–37. http://dx.doi.org/10.1016/S0272-
6963(00)00047-4
Sullivan, G.W., Mcdanold, T.N., & Van Aken, E.M. (2002). Equipment replacement decisions and
lean manufacturing. Robotics and Computer-Integrated Manufacturing, 18, 255-265.
http://dx.doi.org/10.1016/S0736-5845(02)00016-9
Tabucanon, M.T., Batanov, D.N., & Verma, D.K. (1994). Intelligent decision support system
(DSS) for the selection process of alternative machines for flexible manufacturing systems
(FMS). Computers in Industry, 25, 131-143. http://dx.doi.org/10.1016/0166-3615(94)90044-2
Tuzkaya, G., Gulsun, B., Kahraman, C., & Ozgen, D. (2010). An integrated fuzzy multi-criteria
decision making methodology for material handling equipment selection problem and an
application. Expert Systems with Applications, 37, 2853-2863.
http://dx.doi.org/10.1016/j.eswa.2009.09.004
Van Laarhoven, P.J.M., & Pedrcyz, W. (1983). A fuzzy extension of Saaty’s priority theory.
Fuzzy Sets and Systems, 11, 229-241. http://dx.doi.org/10.1016/S0165-0114(83)80082-7
Wang, T.Y., Shaw, C.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. http://dx.doi.org/10.1080/002075400188519
Yilmaz, B., & Dagdeviren, M. (2011). A combined approach for equipment selection:
F-PROMETHEE method and zero–one goal programming. Expert Systems with Applications,
38, 11641-11650. http://dx.doi.org/10.1016/j.eswa.2011.03.043
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate
reasoning-I. Information Sciences, 8(3), 199-249. http://dx.doi.org/10.1016/0020-0255(75)90036-5

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