How to cite this paper
Rezaei, A & Liu, Q. (2024). A multi objective optimization framework for robust and resilient supply chain network design using NSGAII and MOPSO algorithms.International Journal of Industrial Engineering Computations , 15(3), 773-790.
Refrences
Abbasian, M., Sazvar, Z., & Mohammadisiahroudi, M. (2023). A hybrid optimization method to design a sustainable resilient supply chain in a perishable food industry. Environmental Science and Pollution Research, 30(3). https://doi.org/10.1007/s11356-022-22115-8
Agarwal, S., Kant, R., & Shankar, R. (2020). Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach. International Journal of Disaster Risk Reduction, 51. https://doi.org/10.1016/j.ijdrr.2020.101838
Akbari, A., Fathabadi, A., Razmi, M., Zarifian, A., Amiri, M., Ghodsi, A., & Vafadar Moradi, E. (2022). Characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients: A systematic review and meta-analysis. In American Journal of Emergency Medicine (Vol. 52). https://doi.org/10.1016/j.ajem.2021.12.012
Alizadeh Afrouzy, Z., Paydar, M. M., Nasseri, S. H., & Mahdavi, I. (2018). A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development. Journal of Industrial Engineering International, 14(1). https://doi.org/10.1007/s40092-017-0209-7
Arabsheybani, A., & Arshadi Khasmeh, A. (2021). Robust and resilient supply chain network design considering risks in food industry: flavour industry in Iran. International Journal of Management Science and Engineering Management, 16(3). https://doi.org/10.1080/17509653.2021.1907811
Azadnia, A. H., Saman, M. Z. M., & Wong, K. Y. (2015). Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process. In International Journal of Production Research (Vol. 53, Issue 2). https://doi.org/10.1080/00207543.2014.935827
Babaveisi, V., Paydar, M. M., & Safaei, A. S. (2018). Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms. Journal of Industrial Engineering International, 14(2). https://doi.org/10.1007/s40092-017-0217-7
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1). https://doi.org/10.1287/opre.1030.0065
Çebi, F., & Otay, I. (2016). A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Information Sciences, 339. https://doi.org/10.1016/j.ins.2015.12.032
Chaudhry, R., Tapaswi, S., & Kumar, N. (2019). FZ enabled Multi-objective PSO for multicasting in IoT based Wireless Sensor Networks. Information Sciences, 498. https://doi.org/10.1016/j.ins.2019.05.002
Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2. https://doi.org/10.1109/CEC.2002.1004388
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40. https://doi.org/10.1016/j.jocs.2019.101074
Edwards, W., Miles, R. F., & von Winterfeldt, D. (2007). Advances in decision analysis: From foundations to applications. In Advances in Decision Analysis: From Foundations to Applications. https://doi.org/10.1017/CBO9780511611308
Feng, Y., Chen, Y., & Liu, Y. (2023). Optimising two-stage robust supplier selection and order allocation problem under risk-averse criterion. International Journal of Production Research, 61(19). https://doi.org/10.1080/00207543.2022.2127963
Genichi Taguchi. (1986). Introduction to quality engineering: Designing quality into products and processes, G. Taguchi. In Asian productivity organization.
Goli, A., Bakhshi, M., & Babaee Tirkolaee, E. (2020). A Review on Main Challenges of Disaster Relief Supply Chain to Reduce Casualties in Case of Natural Disasters. Journal of Research in Science, Engineering and Technology, 7(02). https://doi.org/10.24200/jrset.vol7iss02pp21-28
Hosseini, S., & Khaled, A. Al. (2019). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1). https://doi.org/10.1007/s10845-016-1241-y
Kaur, H., & Prakash Singh, S. (2021). Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies. International Journal of Production Economics, 231. https://doi.org/10.1016/j.ijpe.2020.107830
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2022). A Fuzzy Simultaneous Evaluation of Criteria and Alternatives (F-SECA) for Sustainable E-Waste Scenario Management. Sustainability (Switzerland), 14(16). https://doi.org/10.3390/su141610371
Liu, Y., Lei, H., Zhang, D., & Wu, Z. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 55. https://doi.org/10.1016/j.apm.2017.10.041
Margolis, J. T., Sullivan, K. M., Mason, S. J., & Magagnotti, M. (2018). A multi-objective optimization model for designing resilient supply chain networks. International Journal of Production Economics, 204. https://doi.org/10.1016/j.ijpe.2018.06.008
Mirzagoltabar, H., Shirazi, B., Mahdavi, I., & Arshadi Khamseh, A. (2023). Integration of sustainable closed-loop supply chain with reliability and possibility of new product development: a robust fuzzy optimisation model. International Journal of Systems Science: Operations and Logistics, 10(1). https://doi.org/10.1080/23302674.2022.2119112
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019). A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design. Computers and Industrial Engineering, 127. https://doi.org/10.1016/j.cie.2018.09.052
Mousavi, S. M., Sadeghi, J., Niaki, S. T. A., & Tavana, M. (2016). A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO. Applied Soft Computing Journal, 43. https://doi.org/10.1016/j.asoc.2016.02.014
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2). https://doi.org/10.1287/opre.43.2.264
Nayeri, S., Ali Torabi, S., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 311. https://doi.org/10.1016/j.jclepro.2021.127691
Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1). https://doi.org/10.1002/j.2158-1592.2010.tb00125.x
Piya, S., Shamsuzzoha, A., & Khadem, M. (2022). Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach. Applied Soft Computing, 121. https://doi.org/10.1016/j.asoc.2022.108756
Ponomarov, S. Y., & Holcomb, M. C. (2009). The International Journal of Logistics Management Understanding the concept of supply chain resilience. The International Journal of Logistics Management The International Journal of Logistics Management Iss International Journal of Physical Distribution & Logistics Management, 20(5).
Robles, J. O., Azzaro-Pantel, C., & Aguilar-Lasserre, A. (2020). Optimization of a hydrogen supply chain network design under demand uncertainty by multi-objective genetic algorithms. Computers and Chemical Engineering, 140. https://doi.org/10.1016/j.compchemeng.2020.106853
Roy, A. (2010). Poverty capital: Microfinance and the making of development. In Poverty Capital: Microfinance and the Making of Development. https://doi.org/10.4324/9780203854716
Sahebjamnia, N. (2020). Resilient supplier selection and order allocation under uncertainty. Scientia Iranica, 27(1 E). https://doi.org/10.24200/SCI.2018.5547.1337
Soyster, A. L. (1973). Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming. Operations Research, 21(5). https://doi.org/10.1287/opre.21.5.1154
Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 113. https://doi.org/10.1016/j.jclepro.2015.10.074
Thevenin, S., Ben-Ammar, O., & Brahimi, N. (2022). Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty. European Journal of Operational Research, 303(3). https://doi.org/10.1016/j.ejor.2022.03.029
Tirkolaee, E. B., Mardani, A., Dashtian, Z., Soltani, M., & Weber, G. W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of Cleaner Production, 250. https://doi.org/10.1016/j.jclepro.2019.119517
Tirkolaee, E. B., Torkayesh, A. E., Tavana, M., Goli, A., Simic, V., & Ding, W. (2023). An integrated decision support framework for resilient vaccine supply chain network design. Engineering Applications of Artificial Intelligence, 126. https://doi.org/10.1016/j.engappai.2023.106945
Vali-Siar, M. M., Roghanian, E., & Jabbarzadeh, A. (2022). Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: A case study. Computers and Industrial Engineering, 172. https://doi.org/10.1016/j.cie.2022.108513
Yazdani, M., Torkayesh, A. E., Chatterjee, P., Fallahpour, A., Montero-Simo, M. J., Araque-Padilla, R. A., & Wong, K. Y. (2022). A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain. Socio-Economic Planning Sciences, 82. https://doi.org/10.1016/j.seps.2022.101257
Agarwal, S., Kant, R., & Shankar, R. (2020). Evaluating solutions to overcome humanitarian supply chain management barriers: A hybrid fuzzy SWARA – Fuzzy WASPAS approach. International Journal of Disaster Risk Reduction, 51. https://doi.org/10.1016/j.ijdrr.2020.101838
Akbari, A., Fathabadi, A., Razmi, M., Zarifian, A., Amiri, M., Ghodsi, A., & Vafadar Moradi, E. (2022). Characteristics, risk factors, and outcomes associated with readmission in COVID-19 patients: A systematic review and meta-analysis. In American Journal of Emergency Medicine (Vol. 52). https://doi.org/10.1016/j.ajem.2021.12.012
Alizadeh Afrouzy, Z., Paydar, M. M., Nasseri, S. H., & Mahdavi, I. (2018). A meta-heuristic approach supported by NSGA-II for the design and plan of supply chain networks considering new product development. Journal of Industrial Engineering International, 14(1). https://doi.org/10.1007/s40092-017-0209-7
Arabsheybani, A., & Arshadi Khasmeh, A. (2021). Robust and resilient supply chain network design considering risks in food industry: flavour industry in Iran. International Journal of Management Science and Engineering Management, 16(3). https://doi.org/10.1080/17509653.2021.1907811
Azadnia, A. H., Saman, M. Z. M., & Wong, K. Y. (2015). Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process. In International Journal of Production Research (Vol. 53, Issue 2). https://doi.org/10.1080/00207543.2014.935827
Babaveisi, V., Paydar, M. M., & Safaei, A. S. (2018). Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms. Journal of Industrial Engineering International, 14(2). https://doi.org/10.1007/s40092-017-0217-7
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1). https://doi.org/10.1287/opre.1030.0065
Çebi, F., & Otay, I. (2016). A two-stage fuzzy approach for supplier evaluation and order allocation problem with quantity discounts and lead time. Information Sciences, 339. https://doi.org/10.1016/j.ins.2015.12.032
Chaudhry, R., Tapaswi, S., & Kumar, N. (2019). FZ enabled Multi-objective PSO for multicasting in IoT based Wireless Sensor Networks. Information Sciences, 498. https://doi.org/10.1016/j.ins.2019.05.002
Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2. https://doi.org/10.1109/CEC.2002.1004388
Davoudabadi, R., Mousavi, S. M., & Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, 40. https://doi.org/10.1016/j.jocs.2019.101074
Edwards, W., Miles, R. F., & von Winterfeldt, D. (2007). Advances in decision analysis: From foundations to applications. In Advances in Decision Analysis: From Foundations to Applications. https://doi.org/10.1017/CBO9780511611308
Feng, Y., Chen, Y., & Liu, Y. (2023). Optimising two-stage robust supplier selection and order allocation problem under risk-averse criterion. International Journal of Production Research, 61(19). https://doi.org/10.1080/00207543.2022.2127963
Genichi Taguchi. (1986). Introduction to quality engineering: Designing quality into products and processes, G. Taguchi. In Asian productivity organization.
Goli, A., Bakhshi, M., & Babaee Tirkolaee, E. (2020). A Review on Main Challenges of Disaster Relief Supply Chain to Reduce Casualties in Case of Natural Disasters. Journal of Research in Science, Engineering and Technology, 7(02). https://doi.org/10.24200/jrset.vol7iss02pp21-28
Hosseini, S., & Khaled, A. Al. (2019). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1). https://doi.org/10.1007/s10845-016-1241-y
Kaur, H., & Prakash Singh, S. (2021). Multi-stage hybrid model for supplier selection and order allocation considering disruption risks and disruptive technologies. International Journal of Production Economics, 231. https://doi.org/10.1016/j.ijpe.2020.107830
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2022). A Fuzzy Simultaneous Evaluation of Criteria and Alternatives (F-SECA) for Sustainable E-Waste Scenario Management. Sustainability (Switzerland), 14(16). https://doi.org/10.3390/su141610371
Liu, Y., Lei, H., Zhang, D., & Wu, Z. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 55. https://doi.org/10.1016/j.apm.2017.10.041
Margolis, J. T., Sullivan, K. M., Mason, S. J., & Magagnotti, M. (2018). A multi-objective optimization model for designing resilient supply chain networks. International Journal of Production Economics, 204. https://doi.org/10.1016/j.ijpe.2018.06.008
Mirzagoltabar, H., Shirazi, B., Mahdavi, I., & Arshadi Khamseh, A. (2023). Integration of sustainable closed-loop supply chain with reliability and possibility of new product development: a robust fuzzy optimisation model. International Journal of Systems Science: Operations and Logistics, 10(1). https://doi.org/10.1080/23302674.2022.2119112
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019). A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design. Computers and Industrial Engineering, 127. https://doi.org/10.1016/j.cie.2018.09.052
Mousavi, S. M., Sadeghi, J., Niaki, S. T. A., & Tavana, M. (2016). A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO. Applied Soft Computing Journal, 43. https://doi.org/10.1016/j.asoc.2016.02.014
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations Research, 43(2). https://doi.org/10.1287/opre.43.2.264
Nayeri, S., Ali Torabi, S., Tavakoli, M., & Sazvar, Z. (2021). A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network. Journal of Cleaner Production, 311. https://doi.org/10.1016/j.jclepro.2021.127691
Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1). https://doi.org/10.1002/j.2158-1592.2010.tb00125.x
Piya, S., Shamsuzzoha, A., & Khadem, M. (2022). Analysis of supply chain resilience drivers in oil and gas industries during the COVID-19 pandemic using an integrated approach. Applied Soft Computing, 121. https://doi.org/10.1016/j.asoc.2022.108756
Ponomarov, S. Y., & Holcomb, M. C. (2009). The International Journal of Logistics Management Understanding the concept of supply chain resilience. The International Journal of Logistics Management The International Journal of Logistics Management Iss International Journal of Physical Distribution & Logistics Management, 20(5).
Robles, J. O., Azzaro-Pantel, C., & Aguilar-Lasserre, A. (2020). Optimization of a hydrogen supply chain network design under demand uncertainty by multi-objective genetic algorithms. Computers and Chemical Engineering, 140. https://doi.org/10.1016/j.compchemeng.2020.106853
Roy, A. (2010). Poverty capital: Microfinance and the making of development. In Poverty Capital: Microfinance and the Making of Development. https://doi.org/10.4324/9780203854716
Sahebjamnia, N. (2020). Resilient supplier selection and order allocation under uncertainty. Scientia Iranica, 27(1 E). https://doi.org/10.24200/SCI.2018.5547.1337
Soyster, A. L. (1973). Technical Note—Convex Programming with Set-Inclusive Constraints and Applications to Inexact Linear Programming. Operations Research, 21(5). https://doi.org/10.1287/opre.21.5.1154
Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: A numerical illustration in electronics industry. Journal of Cleaner Production, 113. https://doi.org/10.1016/j.jclepro.2015.10.074
Thevenin, S., Ben-Ammar, O., & Brahimi, N. (2022). Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty. European Journal of Operational Research, 303(3). https://doi.org/10.1016/j.ejor.2022.03.029
Tirkolaee, E. B., Mardani, A., Dashtian, Z., Soltani, M., & Weber, G. W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of Cleaner Production, 250. https://doi.org/10.1016/j.jclepro.2019.119517
Tirkolaee, E. B., Torkayesh, A. E., Tavana, M., Goli, A., Simic, V., & Ding, W. (2023). An integrated decision support framework for resilient vaccine supply chain network design. Engineering Applications of Artificial Intelligence, 126. https://doi.org/10.1016/j.engappai.2023.106945
Vali-Siar, M. M., Roghanian, E., & Jabbarzadeh, A. (2022). Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: A case study. Computers and Industrial Engineering, 172. https://doi.org/10.1016/j.cie.2022.108513
Yazdani, M., Torkayesh, A. E., Chatterjee, P., Fallahpour, A., Montero-Simo, M. J., Araque-Padilla, R. A., & Wong, K. Y. (2022). A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain. Socio-Economic Planning Sciences, 82. https://doi.org/10.1016/j.seps.2022.101257