How to cite this paper
Sutthibutr, N & Chiadamrong, N. (2019). Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem.Uncertain Supply Chain Management, 7(4), 635-664.
Refrences
Avci, M. G., & Selim, H. (2018). A multi-objective simulation-based optimization approach for inventory replenishment problem with premium freights in convergent supply chains. Omega, 80, 153-165.
Arani, H. V., & Torabi, S. A. (2018). Integrated material-financial supply chain master planning under mixed uncertainty. Information Sciences, 423, 96-114.
Azizi, A., Aikhuele, D. O., & Souleman, F. S. (2015). A Fuzzy TOPSIS Model to Rank Automotive Suppliers. Procedia Manufacturing, 2, 159-164.
Batarfi, R., Jaber, M. Y., & Zanoni, S. (2016). Dual-channel supply chain: A strategy to maximize profit. Applied Mathematical Modelling, 40(21-22), 9454-9473.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141.
Bilir, C., Ekici, S. O., & Ulengin, F. (2017). An integrated multi-objective supply chain network and competitive facility location model. Computers & Industrial Engineering, 108, 136-148.
Bittante, A., Pettersson, F., & Saxén, H. (2018). Optimization of a small-scale LNG supply chain. Energy, 148, 79-89.
Bodjanova, S. (2002). A generalized α-cut. Fuzzy Sets and Systems, 126(2), 157-176.
Chan, F. T., Chung, S. H., & Wadhwa, S. (2005). A hybrid genetic algorithm for production and distribution. Omega, 33(4), 345-355.
Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301.
Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004). Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing, 10(4), 273-297.
Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations. Applied Soft Computing, 69, 232-249.
García-Díaz, J. C., Pulido-Rojano, A., & Giner-Bosch, V. (2017). Bi-objective optimization of a multihead weighing process. European Journal of Industrial Engineering, 11(3), 403-423.
Ghaithan, A. M., Attia, A., & Duffuaa, S. O. (2017). Multi-objective optimization model for a downstream oil and gas supply chain. Applied Mathematical Modelling, 52, 689-708.
Hajghasem, M. (2016). Optimal routing in supply chain aimed at minimizing vehicle cost and supply. Procedia Economics and Finance, 36(1), 353-362.
Hisjam, M., Guritno, A. D., Supriyatno, N., & Tandjung, S. D. (2015). A Sustainable Partnership Model among Supply Chain Players in Wooden Furniture Industry Using Goal Programming. Agriculture and Agricultural Science Procedia, 3, 154-158.
Nezhad, A., Roghanian, E., & Azadi, Z. (2013). A fuzzy goal programming approach to solve multi-objective supply chain network design problems. International Journal of Industrial Engineering Computations, 4(3), 315-324.
Kabak, Ö., & Ülengin, F. (2011). Possibilistic linear-programming approach for supply chain networking decisions. European Journal of Operational Research, 209(3), 253-264.
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314-1328.
Koleva, M. N., Calderón, A. J., Zhang, D., Styan, C. A., & Papageorgiou, L. G. (2018). Integration of environmental aspects in modelling and optimisation of water supply chains. Science of The Total Environment, 636, 314-338.
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69-85.
Kumar, S., Kumar, S., & Barman, A. G. (2018). Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Computer Science, 133, 905-912.
Lai, Y. J., & Hwang, C. L. (1994). Fuzzy multiple objective decision making. In Fuzzy Multiple Objective Decision Making(pp. 139-262). Springer, Berlin, Heidelberg.
Naeni, L. M., & Salehipour, A. (2011). Evaluating fuzzy earned value indices and estimates by applying alpha cuts. Expert Systems with Applications, 38(7), 8193-8198.
Nikolopoulou, A., & Ierapetritou, M. G. (2012). Hybrid simulation based optimization approach for supply chain management. Computers & Chemical Engineering, 47, 183-193.
Nixon, J. D., Dey, P. K., Davies, P. A., Sagi, S., & Berry, R. F. (2014). Supply chain optimisation of pyrolysis plant deployment using goal programming. Energy, 68, 262-271.
Pariazar, M., & Sir, M. Y. (2018). A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality. Computers & Industrial Engineering, 121, 113-130.
Pires, M. C., & Frazzon, E. M. (2016). On the research of linear programming solving methods for non-hierarchical spare parts supply chain planning. IFAC-PapersOnLine, 49(30), 198-203.
Pibernik, R., & Sucky, E. (2007). An approach to inter-domain master planning in supply chains. International Journal of Production Economics, 108(1-2), 200-212.
Pires, M. C., Frazzon, E. M., Danielli, A. M. C., Kück, M., & Freitag, M. (2018). Towards a simulation-based optimization approach to integrate supply chain planning and control. Procedia CIRP, 72, 520-525.
Roy, D., Krishnamurthy, A., Heragu, S. S., & Malmborg, C. J. (2014). Blocking effects in warehouse systems with autonomous vehicles. IEEE Transactions on Automation Science and Engineering, 11(2), 439-451.
Rudberg, M., & Thulin, J. (2009). Centralised supply chain master planning employing advanced planning systems. Production Planning and Control, 20(2), 158-167.
Simić, D., Kovačević, I., Svirčević, V., & Simić, S. (2017). 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review. Journal of Applied Logic, 24, 85-96.
Spitter, J. M., Hurkens, C. A., De Kok, A. G., Lenstra, J. K., & Negenman, E. G. (2005). Linear programming models with planned lead times for supply chain operations planning. European Journal of Operational Research, 163(3), 706-720.
Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 37, 243-264.
Tsai, C. C., Chu, C. H., & Barta, T. A. (1997). Modeling and analysis of a manufacturing cell formation problem with fuzzy mixed-integer programming. IIE transactions, 29(7), 533-547.
Önüt, S., Gülsün, B., Tuzkaya, U. R., & Tuzkaya, G. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information Sciences, 178(2), 485-500.
Vaziri, S., Zaretalab, A., Esmaeili, M., & Niaki, S. T. A. (2018). An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint. Applied Soft Computing, 70, 238-262.
Werro, N. (2015). Fuzzy Classification of Online Customers(Vol. 44). Heidelberg: Springer.
Yang, Y., Li, X. R., & Han, D. (2016). An improved α-cut approach to transforming fuzzy membership function into basic belief assignment. Chinese Journal of Aeronautics, 29(4), 1042-1051.
Zhang, X., Ma, W., & Chen, L. (2014). New similarity of triangular fuzzy number and its application. The Scientific World Journal, 2014.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.
Arani, H. V., & Torabi, S. A. (2018). Integrated material-financial supply chain master planning under mixed uncertainty. Information Sciences, 423, 96-114.
Azizi, A., Aikhuele, D. O., & Souleman, F. S. (2015). A Fuzzy TOPSIS Model to Rank Automotive Suppliers. Procedia Manufacturing, 2, 159-164.
Batarfi, R., Jaber, M. Y., & Zanoni, S. (2016). Dual-channel supply chain: A strategy to maximize profit. Applied Mathematical Modelling, 40(21-22), 9454-9473.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141.
Bilir, C., Ekici, S. O., & Ulengin, F. (2017). An integrated multi-objective supply chain network and competitive facility location model. Computers & Industrial Engineering, 108, 136-148.
Bittante, A., Pettersson, F., & Saxén, H. (2018). Optimization of a small-scale LNG supply chain. Energy, 148, 79-89.
Bodjanova, S. (2002). A generalized α-cut. Fuzzy Sets and Systems, 126(2), 157-176.
Chan, F. T., Chung, S. H., & Wadhwa, S. (2005). A hybrid genetic algorithm for production and distribution. Omega, 33(4), 345-355.
Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics, 102(2), 289-301.
Dubois, D., Foulloy, L., Mauris, G., & Prade, H. (2004). Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Computing, 10(4), 273-297.
Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). A stochastic multi-objective model for a closed-loop supply chain with environmental considerations. Applied Soft Computing, 69, 232-249.
García-Díaz, J. C., Pulido-Rojano, A., & Giner-Bosch, V. (2017). Bi-objective optimization of a multihead weighing process. European Journal of Industrial Engineering, 11(3), 403-423.
Ghaithan, A. M., Attia, A., & Duffuaa, S. O. (2017). Multi-objective optimization model for a downstream oil and gas supply chain. Applied Mathematical Modelling, 52, 689-708.
Hajghasem, M. (2016). Optimal routing in supply chain aimed at minimizing vehicle cost and supply. Procedia Economics and Finance, 36(1), 353-362.
Hisjam, M., Guritno, A. D., Supriyatno, N., & Tandjung, S. D. (2015). A Sustainable Partnership Model among Supply Chain Players in Wooden Furniture Industry Using Goal Programming. Agriculture and Agricultural Science Procedia, 3, 154-158.
Nezhad, A., Roghanian, E., & Azadi, Z. (2013). A fuzzy goal programming approach to solve multi-objective supply chain network design problems. International Journal of Industrial Engineering Computations, 4(3), 315-324.
Kabak, Ö., & Ülengin, F. (2011). Possibilistic linear-programming approach for supply chain networking decisions. European Journal of Operational Research, 209(3), 253-264.
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314-1328.
Koleva, M. N., Calderón, A. J., Zhang, D., Styan, C. A., & Papageorgiou, L. G. (2018). Integration of environmental aspects in modelling and optimisation of water supply chains. Science of The Total Environment, 636, 314-338.
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69-85.
Kumar, S., Kumar, S., & Barman, A. G. (2018). Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit. Procedia Computer Science, 133, 905-912.
Lai, Y. J., & Hwang, C. L. (1994). Fuzzy multiple objective decision making. In Fuzzy Multiple Objective Decision Making(pp. 139-262). Springer, Berlin, Heidelberg.
Naeni, L. M., & Salehipour, A. (2011). Evaluating fuzzy earned value indices and estimates by applying alpha cuts. Expert Systems with Applications, 38(7), 8193-8198.
Nikolopoulou, A., & Ierapetritou, M. G. (2012). Hybrid simulation based optimization approach for supply chain management. Computers & Chemical Engineering, 47, 183-193.
Nixon, J. D., Dey, P. K., Davies, P. A., Sagi, S., & Berry, R. F. (2014). Supply chain optimisation of pyrolysis plant deployment using goal programming. Energy, 68, 262-271.
Pariazar, M., & Sir, M. Y. (2018). A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality. Computers & Industrial Engineering, 121, 113-130.
Pires, M. C., & Frazzon, E. M. (2016). On the research of linear programming solving methods for non-hierarchical spare parts supply chain planning. IFAC-PapersOnLine, 49(30), 198-203.
Pibernik, R., & Sucky, E. (2007). An approach to inter-domain master planning in supply chains. International Journal of Production Economics, 108(1-2), 200-212.
Pires, M. C., Frazzon, E. M., Danielli, A. M. C., Kück, M., & Freitag, M. (2018). Towards a simulation-based optimization approach to integrate supply chain planning and control. Procedia CIRP, 72, 520-525.
Roy, D., Krishnamurthy, A., Heragu, S. S., & Malmborg, C. J. (2014). Blocking effects in warehouse systems with autonomous vehicles. IEEE Transactions on Automation Science and Engineering, 11(2), 439-451.
Rudberg, M., & Thulin, J. (2009). Centralised supply chain master planning employing advanced planning systems. Production Planning and Control, 20(2), 158-167.
Simić, D., Kovačević, I., Svirčević, V., & Simić, S. (2017). 50 years of fuzzy set theory and models for supplier assessment and selection: A literature review. Journal of Applied Logic, 24, 85-96.
Spitter, J. M., Hurkens, C. A., De Kok, A. G., Lenstra, J. K., & Negenman, E. G. (2005). Linear programming models with planned lead times for supply chain operations planning. European Journal of Operational Research, 163(3), 706-720.
Subulan, K., Taşan, A. S., & Baykasoğlu, A. (2015). A fuzzy goal programming model to strategic planning problem of a lead/acid battery closed-loop supply chain. Journal of Manufacturing Systems, 37, 243-264.
Tsai, C. C., Chu, C. H., & Barta, T. A. (1997). Modeling and analysis of a manufacturing cell formation problem with fuzzy mixed-integer programming. IIE transactions, 29(7), 533-547.
Önüt, S., Gülsün, B., Tuzkaya, U. R., & Tuzkaya, G. (2008). A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems. Information Sciences, 178(2), 485-500.
Vaziri, S., Zaretalab, A., Esmaeili, M., & Niaki, S. T. A. (2018). An integrated production and procurement design for a multi-period multi-product manufacturing system with machine assignment and warehouse constraint. Applied Soft Computing, 70, 238-262.
Werro, N. (2015). Fuzzy Classification of Online Customers(Vol. 44). Heidelberg: Springer.
Yang, Y., Li, X. R., & Han, D. (2016). An improved α-cut approach to transforming fuzzy membership function into basic belief assignment. Chinese Journal of Aeronautics, 29(4), 1042-1051.
Zhang, X., Ma, W., & Chen, L. (2014). New similarity of triangular fuzzy number and its application. The Scientific World Journal, 2014.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.