How to cite this paper
Ghassemi, A., Asl-Najafi, J & Yaghoubi, S. (2018). A dynamic bi-objective closed-loop supply chain network design considering supplier selection and remanufacturer subcontractors.Uncertain Supply Chain Management, 6(2), 117-134.
Refrences
Abdollahi, M., Arvan, M., & Razmi, J. (2015). An integrated approach for supplier portfolio selection: Lean or agile?. Expert Systems with Applications, 42(1), 679-690.
Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67-78.
Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782-6791.
Amin, S. H., & Zhang, G. (2013a). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.
Amin, S. H., & Zhang, G. (2013b). A three-stage model for closed-loop supply chain configuration under uncertainty. International Journal of Production Research, 51(5), 1405-1425.
Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A., & Taheri-Moghaddam, A. (2015). A dynamic closed-loop location-inventory problem under disruption risk. Computers & Industrial Engineering, 90, 414-428.
Calabrese, A., Costa, R., & Menichini, T. (2013). Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry. Expert Systems with Applications, 40(9), 3747-3755.
Dickson, G. W. (1966). An analysis of vendor selection and the buying process. Journal of Purchasing, 2(1), 5-17.
Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., & Miranda, S. (2017). The role of uncertainty in supply chains under dynamic modeling. International Journal of Industrial Engineering Computations, 8(1), 119-140.
Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and Operations Management, 10(2), 156-173.
Ghayebloo, S., Tarokh, M. J., Venkatadri, U., & Diallo, C. (2015). Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. Journal of Manufacturing Systems, 36, 76-86.
Görener, A. (2012). Comparing AHP and ANP: an application of strategic decisions making in a manufacturing company. International Journal of Business and Social Science, 3(11).
Haleh, H., & Hamidi, A. (2011). A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon. Expert Systems with Applications, 38(8), 9076-9083.
Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630-2647.
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24.
Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer Berlin Heidelberg.
Jiménez, M. (1996). Ranking fuzzy numbers through the comparison of its expected intervals. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 4(4), 379-388.
Karande, P., & Chakraborty, S. (2013). Using MACBETH method for supplier selection in manufacturing environment. International Journal of Industrial Engineering Computations, 4(2), 259-279.
Krikke, H., Bloemhof-Ruwaard, J., & van Wassenhove, L. N. (2001). Design principles for closed loop supply chains: Optimizing economic, logistics and environmental performance. Erasmus Research Institute of Management, Erasmus Universiteit.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455-465.
Mitchell, S., OSullivan, M., & Dunning, I. (2011). PuLP: a linear programming toolkit for python. The University of Auckland, Auckland, New Zealand.
Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15), 6237-6254.
Mutha, A., & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering, 56(1), 334-346.
Gooran, A., Rafiei, H., & Rabani, M. (2018). Modeling risk and uncertainty in designing reverse logistics problem. Decision Science Letters, 7(1), 13-24.
Nenes, G., & Nikolaidis, Y. (2012). A multi-period model for managing used product returns. International Journal of Production Research, 50(5), 1360-1376.
Nukala, S., & Gupta, S. M. (2007, May). A fuzzy mathematical programming approach for supplier selection in a closed-loop supply chain network. In Proceedings of the 2007 POMS-Dallas meeting (pp. 4-7).
Ozernoy, V. M. (1987). A framework for choosing the most appropriate discrete alternative multiple criteria decision-making method in decision support systems and expert systems. Toward Interactive and Intelligent Decision Support Systems, 2, 56-64.
Ozernoy, V. M. (1992). Choosing The “Best” Multiple Criterlv Decision-Making Method. INFOR: Information Systems and Operational Research, 30(2), 159-171.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1), 328-344.
Saaty, T. L. (1988). What is the analytic hierarchy process?. In Mathematical models for decision support (pp. 109-121). Springer, Berlin, Heidelberg.
Saffar, M. H. S. G., & Razmi, J. (2015). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6(1), 15-32.
Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, 179(3), 1063-1077.
Sharma, S. (2012). Towards a synergy between project and supply chain management. International Journal of Industrial Engineering Computations, 3(5), 931-938.
Singh, S., Jain, S., & Pareek, S. (2014). An economic production model for time dependent demand with rework and multiple production setups. International Journal of Industrial Engineering Computations, 5(2), 305-314.
Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. The International Journal of Advanced Manufacturing Technology, 68(1-4), 917-931.
Thiruchelvam, S., & Tookey, J. E. (2011). Evolving trends of supplier selection criteria and methods. International Journal of Automotive and Mechanical Engineering, 4(1), 437-454.
Yazdani, M. (2014). An integrated MCDM approach to green supplier selection. International Journal of Industrial Engineering Computations, 5(3), 443-458.
Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third-party logistics providers using data envelopment analysis. International Journal of physical distribution & logistics management, 38(4), 262-279.
Zhou, P., Chen, D., & Wang, Q. (2013). Network design and operational modelling for construction green supply chain management. International Journal of Industrial Engineering Computations, 4(1), 13-28.
Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67-78.
Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782-6791.
Amin, S. H., & Zhang, G. (2013a). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165-4176.
Amin, S. H., & Zhang, G. (2013b). A three-stage model for closed-loop supply chain configuration under uncertainty. International Journal of Production Research, 51(5), 1405-1425.
Asl-Najafi, J., Zahiri, B., Bozorgi-Amiri, A., & Taheri-Moghaddam, A. (2015). A dynamic closed-loop location-inventory problem under disruption risk. Computers & Industrial Engineering, 90, 414-428.
Calabrese, A., Costa, R., & Menichini, T. (2013). Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry. Expert Systems with Applications, 40(9), 3747-3755.
Dickson, G. W. (1966). An analysis of vendor selection and the buying process. Journal of Purchasing, 2(1), 5-17.
Fera, M., Fruggiero, F., Lambiase, A., Macchiaroli, R., & Miranda, S. (2017). The role of uncertainty in supply chains under dynamic modeling. International Journal of Industrial Engineering Computations, 8(1), 119-140.
Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and Operations Management, 10(2), 156-173.
Ghayebloo, S., Tarokh, M. J., Venkatadri, U., & Diallo, C. (2015). Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. Journal of Manufacturing Systems, 36, 76-86.
Görener, A. (2012). Comparing AHP and ANP: an application of strategic decisions making in a manufacturing company. International Journal of Business and Social Science, 3(11).
Haleh, H., & Hamidi, A. (2011). A fuzzy MCDM model for allocating orders to suppliers in a supply chain under uncertainty over a multi-period time horizon. Expert Systems with Applications, 38(8), 9076-9083.
Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630-2647.
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16-24.
Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer Berlin Heidelberg.
Jiménez, M. (1996). Ranking fuzzy numbers through the comparison of its expected intervals. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 4(4), 379-388.
Karande, P., & Chakraborty, S. (2013). Using MACBETH method for supplier selection in manufacturing environment. International Journal of Industrial Engineering Computations, 4(2), 259-279.
Krikke, H., Bloemhof-Ruwaard, J., & van Wassenhove, L. N. (2001). Design principles for closed loop supply chains: Optimizing economic, logistics and environmental performance. Erasmus Research Institute of Management, Erasmus Universiteit.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455-465.
Mitchell, S., OSullivan, M., & Dunning, I. (2011). PuLP: a linear programming toolkit for python. The University of Auckland, Auckland, New Zealand.
Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15), 6237-6254.
Mutha, A., & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering, 56(1), 334-346.
Gooran, A., Rafiei, H., & Rabani, M. (2018). Modeling risk and uncertainty in designing reverse logistics problem. Decision Science Letters, 7(1), 13-24.
Nenes, G., & Nikolaidis, Y. (2012). A multi-period model for managing used product returns. International Journal of Production Research, 50(5), 1360-1376.
Nukala, S., & Gupta, S. M. (2007, May). A fuzzy mathematical programming approach for supplier selection in a closed-loop supply chain network. In Proceedings of the 2007 POMS-Dallas meeting (pp. 4-7).
Ozernoy, V. M. (1987). A framework for choosing the most appropriate discrete alternative multiple criteria decision-making method in decision support systems and expert systems. Toward Interactive and Intelligent Decision Support Systems, 2, 56-64.
Ozernoy, V. M. (1992). Choosing The “Best” Multiple Criterlv Decision-Making Method. INFOR: Information Systems and Operational Research, 30(2), 159-171.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637-649.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1), 328-344.
Saaty, T. L. (1988). What is the analytic hierarchy process?. In Mathematical models for decision support (pp. 109-121). Springer, Berlin, Heidelberg.
Saffar, M. H. S. G., & Razmi, J. (2015). A new multi objective optimization model for designing a green supply chain network under uncertainty. International Journal of Industrial Engineering Computations, 6(1), 15-32.
Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. European Journal of Operational Research, 179(3), 1063-1077.
Sharma, S. (2012). Towards a synergy between project and supply chain management. International Journal of Industrial Engineering Computations, 3(5), 931-938.
Singh, S., Jain, S., & Pareek, S. (2014). An economic production model for time dependent demand with rework and multiple production setups. International Journal of Industrial Engineering Computations, 5(2), 305-314.
Soleimani, H., Seyyed-Esfahani, M., & Shirazi, M. A. (2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm. The International Journal of Advanced Manufacturing Technology, 68(1-4), 917-931.
Thiruchelvam, S., & Tookey, J. E. (2011). Evolving trends of supplier selection criteria and methods. International Journal of Automotive and Mechanical Engineering, 4(1), 437-454.
Yazdani, M. (2014). An integrated MCDM approach to green supplier selection. International Journal of Industrial Engineering Computations, 5(3), 443-458.
Zhou, G., Min, H., Xu, C., & Cao, Z. (2008). Evaluating the comparative efficiency of Chinese third-party logistics providers using data envelopment analysis. International Journal of physical distribution & logistics management, 38(4), 262-279.
Zhou, P., Chen, D., & Wang, Q. (2013). Network design and operational modelling for construction green supply chain management. International Journal of Industrial Engineering Computations, 4(1), 13-28.