[1] Olabanji, O. M. (2018). Reconnoitering the suitability of fuzzified weighted decision matrix for design process of a reconfigurable assembly fixture. International journal of design engineering, 8(1), 38-56.
[2] Renzi, C., Leali, F., Pellicciari, M., Andrisano, A. O., & Berselli, G. (2015). Selecting alternatives in the conceptual design phase: an application of Fuzzy-AHP and Pugh’s controlled convergence. International journal on interactive design and manufacturing (IJIDeM), 9(1), 1-17.
[3] Olabanji, O., Mpofu, K., & Battaïa, O. (2016). Design, simulation and experimental investigation of a novel reconfigurable assembly fixture for press brakes. The international journal of advanced manufacturing technology, 82(1-4), 663-679.
[4] Benkamoun, N., Huyet, A. L., & Kouiss, K. (2013, October). Reconfigurable assembly system configuration design approaches for product change. Proceedings of 2013 international conference on industrial engineering and systems management (IESM) (pp. 1-8). IEEE.
[5] Li, Z., Pasek, Z. J., & Adams, J. (2004). Reconfigurable fixtures: concept and examples.Symposium conducted at the meeting of USA symposium on flexible automation, Denver, Colorado.
[6] Yu, K., Wang, S., Wang, Y., & Yang, Z. (2018). A flexible fixture design method research for similar automotive body parts of different automobiles. Advances in mechanical engineering, 10(2), 1687814018761272.
[7] Renzi, C., & Leali, F. (2016). A multicriteria decision‐making application to the conceptual design of mechanical components. Journal of multi‐criteria decision analysis, 23(3-4), 87-111.
[8] Girod, M., Elliott, A. C., Burns, N. D., & Wright, I. C. (2003). Decision making in conceptual engineering design: an empirical investigation. Proceedings of the institution of mechanical engineers, Part B: journal of engineering manufacture, 217(9), 1215-1228.
[9] Kuo, T. C., Huang, S. H., & Zhang, H. C. (2001). Design for manufacture and design for ‘X’: concepts, applications, and perspectives. Computers & industrial engineering, 41(3), 241-260.
[10] Dombrowski, U., Schmidt, S., & Schmidtchen, K. (2014). Analysis and integration of design for X approaches in lean design as basis for a lifecycle optimized product design. Procedia CIRP, 15, 385-390.
[11] Skander, A., Roucoules, L., & Meyer, J. S. K. (2008). Design and manufacturing interface modelling for manufacturing processes selection and knowledge synthesis in design. The international journal of advanced manufacturing technology, 37(5-6), 443-454.
[12] Harari, N. S., Fundin, A., & Carlsson, A. L. (2018). Components of the design process of flexible and reconfigurable assembly systems. Procedia manufacturing, 25, 549-556.
[13] Renzi, C., Leali, F., & Di Angelo, L. (2017). A review on decision-making methods in engineering design for the automotive industry. Journal of engineering design, 28(2), 118-143.
[14] Olabanji, O. M., & Mpofu, K. (2014). Comparison of weighted decision matrix, and analytical hierarchy process for CAD design of reconfigurable assembly fixture. Procedia CIRP, 23, 264-269.
[15] Okudan, G. E., & Shirwaiker, R. A. (2006, July). A multi-stage problem formulation for concept selection for improved product design. 2006 technology management for the global future-PICMET 2006 conference (Vol. 6, pp. 2528-2538). IEEE.
[16] Okudan, G. E., & Tauhid, S. (2008). Concept selection methods–a literature review from 1980 to 2008. International journal of design engineering, 1(3), 243-277.
[17] Wang, J. (2001). Ranking engineering design concepts using a fuzzy outranking preference model. Fuzzy sets and systems, 119(1), 161-170.
[18] Olabanji, O., & Mpofu, K. (2020). Pugh matrix and aggregated by extent analysis using trapezoidal fuzzy number for assessing conceptual designs. Decision science letters, 9(1), 21-36.
[19] Goyal, K. K., Jain, P. K., & Jain, M. (2012). Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. International journal of production research, 50(15), 4175-4191.
[20] Youssef, A. M., & ElMaraghy, H. A. (2007). Optimal configuration selection for reconfigurable manufacturing systems. International journal of flexible manufacturing systems, 19(2), 67-106.
[21] Ashraf, M., & Hasan, F. (2018). Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology, 98(5-8), 2137-2156.
[22] Gupta, A., Jain, P. K., & Kumar, D. (2015). Configuration selection of reconfigurable manufacturing system based on performance. International journal of industrial and systems engineering, 20(2), 209-230.
[23] Rehman, A. U. (2017). Multi-criteria grey relational approach to evaluating reconfigurable manufacturing configurations. South African Journal of industrial engineering, 28(1), 36-46.
[24] Renna, P. (2017). Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model. Journal of manufacturing systems, 45, 149-158.
[25] Yi, G., Wang, Y., & Zhao, X. (2018). Evaluation and optimization of the design schemes of reconfigurable machine tools based on multiple-attribute decision-making. Advances in mechanical engineering, 10(12), 1687814018813054.
[26] Mokhtarian, M. N. (2011). A new fuzzy weighted average (FWA) method based on left and right scores: An application for determining a suitable location for a gas oil station. Computers & mathematics with applications, 61(10), 3136-3145.
[27] Wang, J. (2002). Improved engineering design concept selection using fuzzy sets. International journal of computer integrated manufacturing, 15(1), 18-27.
[28] Yeo, S. H., Mak, M. W., & Balon, S. A. P. (2004). Analysis of decision-making methodologies for desirability score of conceptual design. Journal of engineering design, 15(2), 195-208.
[29] Balin, A., Demirel, H., & Alarcin, F. (2016). A novel hybrid MCDM model based on fuzzy AHP and fuzzy TOPSIS for the most affected gas turbine component selection by the failures. Journal of marine engineering & technology, 15(2), 69-78.
[30] Alarcin, F., Balin, A., & Demirel, H. (2014). Fuzzy AHP and fuzzy TOPSIS integrated hybrid method for auxiliary systems of ship main engines. Journal of marine engineering & technology, 13(1), 3-11.
[31] Nazam, M., Xu, J., Tao, Z., Ahmad, J., & Hashim, M. (2015). A fuzzy AHP-TOPSIS framework for the risk assessment of green supply chain implementation in the textile industry. International journal of supply and operations management, 2(1), 548-568.
[32] Chakraborty, K., Mondal, S., & Mukherjee, K. (2017). Analysis of product design characteristics for remanufacturing using fuzzy AHP and axiomatic design. Journal of engineering design, 28(5), 338-368.
[33] Aryanezhad, M., Tarokh, M. J., Mokhtarian, M. N., & Zaheri, F. (2011). A fuzzy TOPSIS method based on left and right scores. International journal of industrial engineering & production research, 22(1), 53-60.
[34] [Amiri-Aref, M., Javadian, N., & Kazemi, M. (2012). A new fuzzy positive and negative ideal solution for fuzzy TOPSIS. WSEAS Transactions on Circuits and Systems, 11(3), 92-103.
[35] Mokhtarian, M. N., & Hadi-Vencheh, A. (2012). A new fuzzy TOPSIS method based on left and right scores: An application for determining an industrial zone for dairy products factory. Applied soft computing, 12(8), 2496-2505.
[36] Olabanji, O. M. (2015).
Development of a reconfigurable assembly system for the assembly of press brakes (Doctoral Thesis in the Department of industrial Engineering, Tshwane University of Technology, Pretoria West, South Africa). Retrieved from
https://books.google.com.ng/books?id=5VNDAQAACAAJ
[37] Olabanji, O. M., & Mpofu, K. (2019). Adopting hybridized multicriteria decision model as a decision tool in engineering design. Journal of engineering, design and technology, 18(2).