How to cite this paper
Vali-Siar, M., Shekarabi, H & Roghanian, E. (2024). A novel multi-objective stochastic model and a novel hybrid metaheuristic for designing supply chain network under disruption risks to enhance supply chain resilience.Decision Science Letters , 13(4), 921-950.
Refrences
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Arabi, M., & Gholamian, M. R. (2023). Resilient closed-loop supply chain network design considering quality uncertainty: A case study of stone quarries. Resources Policy, 80, 103290.
Azad, N., Saharidis, G. K., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Annals of Operations Research, 210(1), 125-163.
Boronoos, M., Mousazadeh, M., & Torabi, S. A. (2021). A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design. Environment, Development and Sustainability, 23(3), 3368-3395.
Bottani, E., Murino, T., Schiavo, M., & Akkerman, R. (2019). Resilient food supply chain design: Modelling framework and metaheuristic solution approach. Computers & Industrial Engineering, 135, 177-198.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
Dehshiri, S. J. H., & Amiri, M. (2024). Considering the circular economy for designing closed-loop supply chain under hybrid uncertainty: A robust scenario-based possibilistic-stochastic programming. Expert Systems with Applications, 238, 121745.
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
Dorigo, M., Di Caro, G., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial life, 5(2), 137-172.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41.
Fahimnia, B., Jabbarzadeh, A., & Sarkis, J. (2018). Greening versus resilience: A supply chain design perspective. Transportation Research Part E: Logistics and Transportation Review, 119, 129-148.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2018). A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Computers & Operations Research, 100, 314-332.
Fazli-Khalaf, M., Naderi, B., Mohammadi, M., & Pishvaee, M. S. (2021). The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry. Environment, Development and Sustainability, 23(7), 9949-9973.
Feitó-Cespón, M., Costa, Y., Pishvaee, M. S., & Cespón-Castro, R. (2021). A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign. Expert Systems with Applications, 165, 113906.
Feitó-Cespón, M., Sarache, W., Piedra-Jimenez, F., & Cespón-Castro, R. (2017). Redesign of a sustainable reverse supply chain under uncertainty: A case study. Journal of cleaner production, 151, 206-217.
Gen, M., Altiparmak, F., & Lin, L. (2006). A genetic algorithm for two-stage transportation problem using priority-based encoding. OR spectrum, 28(3), 337-354.
Ghavamifar, A., Makui, A., & Taleizadeh, A. A. (2018). Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review, 115, 87-109.
Gholami-Zanjani, S. M., Jabalameli, M. S., & Pishvaee, M. S. (2021). A resilient-green model for multi-echelon meat supply chain planning. Computers & Industrial Engineering, 152, 107018.
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Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research, 263(1), 108-141.
Govindan, K., Paam, P., & Abtahi, A.-R. (2016). A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecological indicators, 67, 753-768.
Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: a real-life case study. Journal of Cleaner Production, 278, 123199.
Holland, J. H. (1975). Adaption in natural and adaptive systems. In: University of Michigan Press, Ann Arbor.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915.
Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: sustainability analysis under disruption risks. International Journal of Production Research, 56(17), 5945-5968.
Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191.
Karmaker, C. L., Ahmed, T., Ahmed, S., Ali, S. M., Moktadir, M. A., & Kabir, G. (2021). Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model. Sustainable production and consumption, 26, 411-427.
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Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
Kumar, P., Herbert, M., & Rao, S. (2017a). Population based metaheuristic algorithm approach for analysis of multi-item multi-period procurement lot sizing problem. Advances in Operations Research, 2017.
Kumar, P., Herbert, M., & Rao, S. (2017b). Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem. Advances in Operations Research, 2017, 1-18. https://EconPapers.repec.org/RePEc:hin:jnlaor:3601217
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Mehrjerdi, Y. Z., & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of cleaner production, 289, 125141.
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Bottani, E., Murino, T., Schiavo, M., & Akkerman, R. (2019). Resilient food supply chain design: Modelling framework and metaheuristic solution approach. Computers & Industrial Engineering, 135, 177-198.
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Dehshiri, S. J. H., & Amiri, M. (2024). Considering the circular economy for designing closed-loop supply chain under hybrid uncertainty: A robust scenario-based possibilistic-stochastic programming. Expert Systems with Applications, 238, 121745.
Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph. D. Thesis, Politecnico di Milano.
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Fahimnia, B., Jabbarzadeh, A., & Sarkis, J. (2018). Greening versus resilience: A supply chain design perspective. Transportation Research Part E: Logistics and Transportation Review, 119, 129-148.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2018). A multi-stage stochastic program for supply chain network redesign problem with price-dependent uncertain demands. Computers & Operations Research, 100, 314-332.
Fazli-Khalaf, M., Naderi, B., Mohammadi, M., & Pishvaee, M. S. (2021). The design of a resilient and sustainable maximal covering closed-loop supply chain network under hybrid uncertainties: a case study in tire industry. Environment, Development and Sustainability, 23(7), 9949-9973.
Feitó-Cespón, M., Costa, Y., Pishvaee, M. S., & Cespón-Castro, R. (2021). A fuzzy inference based scenario building in two-stage optimization framework for sustainable recycling supply chain redesign. Expert Systems with Applications, 165, 113906.
Feitó-Cespón, M., Sarache, W., Piedra-Jimenez, F., & Cespón-Castro, R. (2017). Redesign of a sustainable reverse supply chain under uncertainty: A case study. Journal of cleaner production, 151, 206-217.
Gen, M., Altiparmak, F., & Lin, L. (2006). A genetic algorithm for two-stage transportation problem using priority-based encoding. OR spectrum, 28(3), 337-354.
Ghavamifar, A., Makui, A., & Taleizadeh, A. A. (2018). Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review, 115, 87-109.
Gholami-Zanjani, S. M., Jabalameli, M. S., & Pishvaee, M. S. (2021). A resilient-green model for multi-echelon meat supply chain planning. Computers & Industrial Engineering, 152, 107018.
Ghomi-Avili, M., Naeini, S. G. J., Tavakkoli-Moghaddam, R., & Jabbarzadeh, A. (2018). A fuzzy pricing model for a green competitive closed-loop supply chain network design in the presence of disruptions. Journal of Cleaner Production, 188, 425-442.
Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research, 263(1), 108-141.
Govindan, K., Paam, P., & Abtahi, A.-R. (2016). A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecological indicators, 67, 753-768.
Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: a real-life case study. Journal of Cleaner Production, 278, 123199.
Holland, J. H. (1975). Adaption in natural and adaptive systems. In: University of Michigan Press, Ann Arbor.
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915.
Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: sustainability analysis under disruption risks. International Journal of Production Research, 56(17), 5945-5968.
Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & industrial engineering, 116, 178-191.
Karmaker, C. L., Ahmed, T., Ahmed, S., Ali, S. M., Moktadir, M. A., & Kabir, G. (2021). Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model. Sustainable production and consumption, 26, 411-427.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95-international conference on neural networks,
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. https://doi.org/doi:10.1126/science.220.4598.671
Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust value-creating supply chain networks: a critical review. European Journal of Operational Research, 203(2), 283-293.
Kumar, P., Herbert, M., & Rao, S. (2017a). Population based metaheuristic algorithm approach for analysis of multi-item multi-period procurement lot sizing problem. Advances in Operations Research, 2017.
Kumar, P., Herbert, M., & Rao, S. (2017b). Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem. Advances in Operations Research, 2017, 1-18. https://EconPapers.repec.org/RePEc:hin:jnlaor:3601217
Li, Z., & Zhang, C. (2024). Designing a two-stage model for the resilient agri-food supply chain network under dynamic competition. British Food Journal, 126(2), 662-681.
Liao, T., Socha, K., de Oca, M. A. M., Stützle, T., & Dorigo, M. (2013). Ant colony optimization for mixed-variable optimization problems. IEEE transactions on evolutionary computation, 18(4), 503-518.
Mardan, E., Govindan, K., Mina, H., & Gholami-Zanjani, S. M. (2019). An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem. Journal of Cleaner Production, 235, 1499-1514.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Mehrjerdi, Y. Z., & Shafiee, M. (2021). A resilient and sustainable closed-loop supply chain using multiple sourcing and information sharing strategies. Journal of cleaner production, 289, 125141.
Meng, J., Hu, X., Chen, P., Coffman, D. M., & Han, M. (2020). The unequal contribution to global energy consumption along the supply chain. Journal of environmental management, 268, 110701.
Michalewicz, Z., Vignaux, G. A., & Hobbs, M. (1991). A nonstandard genetic algorithm for the nonlinear transportation problem. ORSA Journal on computing, 3(4), 307-316.
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 & Industrial Engineering, 127, 297-312.
Mohtashami, Z., Aghsami, A., & Jolai, F. (2020). A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption. Journal of cleaner production, 242, 118452.
Moncayo-Martínez, L. A., & Zhang, D. Z. (2011). Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design. International Journal of Production Economics, 131(1), 407-420.
Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360.
Nikian, A., Khademi Zare, H., Lotfi, M. M., & Fallah Nezhad, M. S. (2023). Redesign of a sustainable and resilient closed-loop supply chain network under uncertainty and disruption caused by sanctions and COVID-19. Operations Management Research, 16(2), 1019-1042.
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications, 42(5), 2615-2623.
Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45(8), 1190-1211.
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