Robust mathematical model for supply chain optimization A comprehensive study
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Published:
Jun 30, 2023
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Cost minimization, Decision-making, Mathematical model, Supply chain optimization, Uncertainty
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Abstract
This research provides a comprehensive review of existing literature and research on supply chain optimization, aiming to capture the advances made in the field and identify emerging perspectives. Supply chain optimization plays a vital role in improving operational efficiency, reducing costs, and enhancing customer satisfaction. By analyzing a wide range of studies, this review examines various approaches, models, and techniques used in supply chain optimization, including mathematical programming, stochastic programming, simulation, and metaheuristic algorithms. The review also encompasses key aspects such as demand forecasting, inventory management, production planning, transportation, and distribution network design. Furthermore, the study investigates recent trends, such as incorporating sustainability considerations, addressing uncertainties and risks, and utilizing real-time data and decision support systems. By identifying the gaps and limitations in the existing research, this review sets the stage for future investigations and provides valuable insights for researchers and practitioners seeking to advance supply chain optimization efforts. The findings of this review contribute to enhancing the understanding of supply chain optimization and provide a roadmap for future research directions in this dynamic and critical field
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Pujiastuti, L., Wahyudi, M. ., Conceição, B. J. da ., & Riandari, F. . (2023). Robust mathematical model for supply chain optimization: A comprehensive study. Journal of Intelligent Decision Support System (IDSS), 6(2), 57-65. https://doi.org/10.35335/idss.v6i2.137
References
Babbar, C., & Amin, S. H. (2018). A multi-objective mathematical model integrating environmental concerns for supplier selection and order allocation based on fuzzy QFD in beverages industry. Expert Systems with Applications, 92, 27–38.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202.
Becerra, P., Mula, J., & Sanchis, R. (2022). Sustainable inventory management in supply chains: Trends and further research. Sustainability, 14(5), 2613.
Bentalha, B., Hmioui, A., & Alla, L. (2019). The digitalization of the supply chain management of service companies: a prospective approach. Proceedings of the 4th International Conference on Smart City Applications, 1–8.
Carvalho, H., Govindan, K., Azevedo, S. G., & Cruz-Machado, V. (2017). Modelling green and lean supply chains: An eco-efficiency perspective. Resources, Conservation and Recycling, 120, 75–87.
Chen, I. J., & Paulraj, A. (2004). Understanding supply chain management: critical research and a theoretical framework. International Journal of Production Research, 42(1), 131–163.
Coenen, J., van Der Heijden, R. E. C. M., & van Riel, A. C. R. (2018). Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions. Journal of Cleaner Production, 201, 1–13.
Cooper, M. C., Lambert, D. M., & Pagh, J. D. (1997). Supply chain management: more than a new name for logistics. The International Journal of Logistics Management, 8(1), 1–14.
D’heur, M. (2015). Sustainable value chain management. Delivering Sustainability.
Datta, S., Granger, C. W. J., Barari, M., & Gibbs, T. (2007). Management of supply chain: an alternative modelling technique for forecasting. Journal of the Operational Research Society, 58(11), 1459–1469.
Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2019). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research, 283, 1079–1109.
Fewings, P., & Henjewele, C. (2019). Construction project management: an integrated approach. Routledge.
Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert Systems with Applications, 116, 454–471.
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.
Ivanov, D. (2017). Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1), 24–43.
Jabbarzadeh, A., Haughton, M., & Pourmehdi, F. (2019). A robust optimization model for efficient and green supply chain planning with postponement strategy. International Journal of Production Economics, 214, 266–283.
Lambert, D. M. (2017). The supply chain management and logistics controversy. In Handbook of logistics and supply-chain management (Vol. 2, pp. 99–126). Emerald Group Publishing Limited.
Langley, C. J., Novack, R. A., Gibson, B., & Coyle, J. J. (2020). Supply chain management: a logistics perspective. Cengage Learning.
Mangla, S. K., Luthra, S., Mishra, N., Singh, A., Rana, N. P., Dora, M., & Dwivedi, Y. (2018). Barriers to effective circular supply chain management in a developing country context. Production Planning & Control, 29(6), 551–569.
Mani, V., Delgado, C., Hazen, B. T., & Patel, P. (2017). Mitigating supply chain risk via sustainability using big data analytics: Evidence from the manufacturing supply chain. Sustainability, 9(4), 608.
Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1–2), 231–249.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, 17–44.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.
Rauniyar, K., Wu, X., Gupta, S., Modgil, S., & de Sousa Jabbour, A. B. L. (2022). Risk management of supply chains in the digital transformation era: contribution and challenges of blockchain technology. Industrial Management & Data Systems, 123(1), 253–277.
Richards, G. (2017). Warehouse management: a complete guide to improving efficiency and minimizing costs in the modern warehouse. Kogan Page Publishers.
Sangaiah, A. K., Tirkolaee, E. B., Goli, A., & Dehnavi-Arani, S. (2020). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft Computing, 24, 7885–7905.
Sreedevi, R., & Saranga, H. (2017). Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation. International Journal of Production Economics, 193, 332–342.
Vanbrabant, L., Verdonck, L., Mertens, S., & Caris, A. (2023). Improving hospital material supply chain performance by integrating decision problems: A literature review and future research directions. Computers & Industrial Engineering, 109235.
Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: a review and implications for future research. The International Journal of Logistics Management, 27(2), 395–417.
Yi, C. Y., Ngai, E. W. T., & Moon, K. (2011). Supply chain flexibility in an uncertain environment: exploratory findings from five case studies. Supply Chain Management: An International Journal, 16(4), 271–283.
Zandieh, M., & Aslani, B. (2019). A hybrid MCDM approach for order distribution in a multiple-supplier supply chain: A case study. Journal of Industrial Information Integration, 16, 100104.
Zeng, L., Liu, S. Q., Kozan, E., Corry, P., & Masoud, M. (2021). A comprehensive interdisciplinary review of mine supply chain management. Resources Policy, 74, 102274.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202.
Becerra, P., Mula, J., & Sanchis, R. (2022). Sustainable inventory management in supply chains: Trends and further research. Sustainability, 14(5), 2613.
Bentalha, B., Hmioui, A., & Alla, L. (2019). The digitalization of the supply chain management of service companies: a prospective approach. Proceedings of the 4th International Conference on Smart City Applications, 1–8.
Carvalho, H., Govindan, K., Azevedo, S. G., & Cruz-Machado, V. (2017). Modelling green and lean supply chains: An eco-efficiency perspective. Resources, Conservation and Recycling, 120, 75–87.
Chen, I. J., & Paulraj, A. (2004). Understanding supply chain management: critical research and a theoretical framework. International Journal of Production Research, 42(1), 131–163.
Coenen, J., van Der Heijden, R. E. C. M., & van Riel, A. C. R. (2018). Understanding approaches to complexity and uncertainty in closed-loop supply chain management: Past findings and future directions. Journal of Cleaner Production, 201, 1–13.
Cooper, M. C., Lambert, D. M., & Pagh, J. D. (1997). Supply chain management: more than a new name for logistics. The International Journal of Logistics Management, 8(1), 1–14.
D’heur, M. (2015). Sustainable value chain management. Delivering Sustainability.
Datta, S., Granger, C. W. J., Barari, M., & Gibbs, T. (2007). Management of supply chain: an alternative modelling technique for forecasting. Journal of the Operational Research Society, 58(11), 1459–1469.
Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2019). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research, 283, 1079–1109.
Fewings, P., & Henjewele, C. (2019). Construction project management: an integrated approach. Routledge.
Ghahremani-Nahr, J., Kian, R., & Sabet, E. (2019). A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm. Expert Systems with Applications, 116, 454–471.
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.
Ivanov, D. (2017). Simulation-based single vs. dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management, 11(1), 24–43.
Jabbarzadeh, A., Haughton, M., & Pourmehdi, F. (2019). A robust optimization model for efficient and green supply chain planning with postponement strategy. International Journal of Production Economics, 214, 266–283.
Lambert, D. M. (2017). The supply chain management and logistics controversy. In Handbook of logistics and supply-chain management (Vol. 2, pp. 99–126). Emerald Group Publishing Limited.
Langley, C. J., Novack, R. A., Gibson, B., & Coyle, J. J. (2020). Supply chain management: a logistics perspective. Cengage Learning.
Mangla, S. K., Luthra, S., Mishra, N., Singh, A., Rana, N. P., Dora, M., & Dwivedi, Y. (2018). Barriers to effective circular supply chain management in a developing country context. Production Planning & Control, 29(6), 551–569.
Mani, V., Delgado, C., Hazen, B. T., & Patel, P. (2017). Mitigating supply chain risk via sustainability using big data analytics: Evidence from the manufacturing supply chain. Sustainability, 9(4), 608.
Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1–2), 231–249.
Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, 17–44.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.
Rauniyar, K., Wu, X., Gupta, S., Modgil, S., & de Sousa Jabbour, A. B. L. (2022). Risk management of supply chains in the digital transformation era: contribution and challenges of blockchain technology. Industrial Management & Data Systems, 123(1), 253–277.
Richards, G. (2017). Warehouse management: a complete guide to improving efficiency and minimizing costs in the modern warehouse. Kogan Page Publishers.
Sangaiah, A. K., Tirkolaee, E. B., Goli, A., & Dehnavi-Arani, S. (2020). Robust optimization and mixed-integer linear programming model for LNG supply chain planning problem. Soft Computing, 24, 7885–7905.
Sreedevi, R., & Saranga, H. (2017). Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation. International Journal of Production Economics, 193, 332–342.
Vanbrabant, L., Verdonck, L., Mertens, S., & Caris, A. (2023). Improving hospital material supply chain performance by integrating decision problems: A literature review and future research directions. Computers & Industrial Engineering, 109235.
Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: a review and implications for future research. The International Journal of Logistics Management, 27(2), 395–417.
Yi, C. Y., Ngai, E. W. T., & Moon, K. (2011). Supply chain flexibility in an uncertain environment: exploratory findings from five case studies. Supply Chain Management: An International Journal, 16(4), 271–283.
Zandieh, M., & Aslani, B. (2019). A hybrid MCDM approach for order distribution in a multiple-supplier supply chain: A case study. Journal of Industrial Information Integration, 16, 100104.
Zeng, L., Liu, S. Q., Kozan, E., Corry, P., & Masoud, M. (2021). A comprehensive interdisciplinary review of mine supply chain management. Resources Policy, 74, 102274.
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