Application of MADM methods as MOORA and WEDBA for ranking of FMS flexibility

Abstract: Flexibility has been cited as a key factor to enhance the performance of flexible manufacturing sys-tem (FMS). The main aim of this paper is to rank the flexibility of FMS. The ranking decisions are complex in the manufacturing field to analyze a number of alternatives based on a set of some attributes. In this research, two MADM methods i.e. MOORA (i.e. multi-objective optimization on the basis of ratio analysis) and weighted Euclidean distance based approach (WEDBA) are used for ranking of flexibility in FMS for new part development. MOORA approach can give de-cision with or without considering relative importance of attributes i.e. attribute weights. While in WEDBA, integrated attribute weights are used for evaluation which included the subjective and objective weights of attributes. Objective weights are calculated by entropy method and subjective weights are calculated by analytic hierarchy process. MOORA is applied in two ways i.e. ratio based and reference point analysis. Ranking of fifteen flexibility of FMS done on the basis fifteen variables which effect flexibility of FMS. The results of MOORA and WEDBA approach shows that product flexibility has the top most flexibility in fifteen flexibilities and programme flexibility has the least impact in fifteen flexibilities.

How to cite this paper

 Jain, V & Ajmera, P. (2019). Application of MADM methods as MOORA and WEDBA for ranking of FMS flexibility.International Journal of Data and Network Science, 3(2), 119-136.

Refrences

Attri, R., & Grover, S. (2014). Decision making over the production system life cycle: MOORA method. International Journal of System Assurance Engineering and Management, 5(3), 320-328.
Brauers, W. K. (2004). Optimization Methods for a Stakeholder Society. Boston: Kluwer Academic.
Brauers, W. K. M. (2013). Multi-objective seaport planning by MOORA decision making. Annals of Operations Research, 206(1), 39-58.
Brauers, W. K. M., & Zavadskas, E. K. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 445.
Brauers, W. K. M., & Zavadskas, E. K. (2009). Robustness of the multi-objective MOORA method with a test for the facilities sector. Technological and Economic Development of Economy, 15(2), 352-375.
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5-24.
Brauers, W. K. M., & Zavadskas, E. K. (2011). MULTIMOORA optimization used to decide on a bank loan to buy property. Technological and Economic Development of Economy, 17(1), 174-188.
Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1155-1166.
Chand, M., Raj, T., & Shankar, R. (2014). A comparative study of multi criteria decision making approaches for risks assessment in supply chain. International Journal of Business Information Systems, 18(1), 67-84.
Chen, I. J., & Chung, C. H. (1996). An examination of flexibility measurements and performance of flexible manufacturing systems. International Journal of Production Research, 34(2), 379-394.
Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. Heidelberg: Springer Berlin.
Das, S. K. (1996). The measurement of flexibility in manufacturing systems. International Journal of Flexible Manufacturing Systems, 8(1), 67-93.
Dattorro, J. (2005). Convex optimization & Euclidean distance geometry. USA: Meboo Publishing.
Gadakh, V., Shinde, V., & Khemnar, N. (2013). Optimization of welding process parameters using MOORA method. The International Journal of Advanced Manufacturing Technology, 69(9-12), 2031-2039.
Ginevičius, R., Brauers, W. K. M., & Podvezko, V. (2010). Regional development in Lithuania considering multiple objectives by the MOORA method. Technological and Economic Development of Economy(4), 613-640.
Gower, J. C. (1982). Euclidean distance geometry. Math. Scientist, 7(1), 1-14.
Jain, V. (2018). Application of combined MADM methods as MOORA and PSI for ranking of FMS performance factors. Benchmarking: An International Journal, 25(6), 1903-1920.
Jain, V., & Raj, T. (2013a). Evaluating the Variables Affecting Flexibility in FMS by Exploratory and Confirmatory Factor Analysis. Global Journal of Flexible Systems Management, 14(4), 181-193. doi: 10.1007/s40171-013-0042-9
Jain, V., & Raj, T. (2013b). Evaluation of flexibility in FMS using SAW and WPM. Decision Science Letters, 2(4), 223-230.
Jain, V., & Raj, T. (2013c). Ranking of Flexibility in Flexible Manufacturing System by Using a Combined Multiple Attribute Decision Making Method. Global Journal of Flexible Systems Management, 14(3), 125-141. doi: http://dx.doi.org/10.1007/s40171-013-0038-5
Jain, V., & Raj, T. (2014a). Evaluation of flexibility in FMS by VIKOR methodology. International Journal of Industrial and Systems Engineering, 18(4), 483-498.
Jain, V., & Raj, T. (2014b). Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach. Frontiers of Mechanical Engineering, 9(3), 218-232.
Jain, V., & Raj, T. (2015a). Evaluating the intensity of variables affecting flexibility in FMS by graph theory and matrix approach. International Journal of Industrial and Systems Engineering, 19(2), 137-154.
Jain, V., & Raj, T. (2015b). A hybrid approach using ISM and modified TOPSIS for the evaluation of flexibility in FMS. International Journal of Industrial and Systems Engineering, 19(3), 389–406.
Jain, V., & Raj, T. (2015c). Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC. International Journal of System Assurance Engineering and Management, 6(3), 350-371.
Jain, V., & Raj, T. (2016). Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach. International journal of production economics, 171(1), 84-96.
Jain, V., & Raj, T. (2017). Tool life management of unmanned production system based on surface roughness by ANFIS. International Journal of System Assurance Engineering and Management, 8(2), 458–467.
Jain, V., & Raj, T. (2018). Identification of performance variables which affect the FMS: a state-of-the-art review. International Journal of Process Management and Benchmarking, 8(4), 470-489.
Karande, P., & Chakraborty, S. (2012). Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection. Materials & Design, 37, 317-324.
Raj, T., Attri, R., & Jain, V. (2012). Modelling the factors affecting flexibility in FMS. International Journal of Industrial and Systems Engineering, 11(4), 350-374.
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. (2013). Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods (Vol. 2). London: Springer-Verlag.
Rao, R. V., & Singh, D. (2011). Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. International Journal of Decision Sciences, Risk and Management, 3(1-2), 32-53.
Rao, R. V., & Singh, D. (2012). Weighted Euclidean distance based approach as a multiple attribute decision making method for plant or facility layout design selection. International Journal of Industrial Engineering Computations, 3(3), 365-382.
Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process (Vol. 6). Pittsburgh, PA: RWS Publications.