Integrating time and cost in dynamic optimization of supply chain recovery

Document Type : Research Paper


School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran


The occurrence of disruptions has undeniable impacts on supply chain (SC) performance and severely affects its costs and revenues. SC resilience (SCR) reduces the impacts of these disruptions. Among the issues in the SCR, although the recovery of the SC after the disruption is of vital importance, it has not been considered as it should be. To fill this gap, this paper enumerates some important issues in SC recovery planning and proposes a dynamic model for it. One of the features of the proposed model is to consider the recovery time and cost in order to achieve the pre-disruption SC performance. Then, we demonstrate the application of this model in the recovery of a two-echelon poultry SC. Since the developed model is a nonlinear dynamic model, we use the direct collocation method to solve it. The outputs of the sensitivity analysis show that changes in many parameters result in significant changes in model variables. Based on the results, it can be said that the development of appropriate models for recovery plays an important role in the analysis of possible alternatives for SC recovery and can help SC managers to deal with disruptions by comparing alternative recovery options.


Main Subjects

Adtiya, S., Kumar, S., Kumar, A., Datta, S., & Mahapatra, S. (2014). A decision support system towards suppliers’ selection in resilient supply chain: Exploration of fuzzy-TOPSIS. International Journal of Management and International Business Studies, 4 (2), 159–168.
Allen, P., Datta, P., & Christopher, M. (2006). Improving the resilience and performance of organizations using multi-agent modelling of a complex production–distribution systems. Risk Management, 8, 294–309.
Betts, J. T. (2010). Practical methods for optimal control and estimation using nonlinear programming. Philadelphia: SIAM.
Chakraborty, T., Chauhan, S. S., & Ouhimmou, M. (2020). Mitigating supply disruption with a backup supplier under uncertain demand: Competition vs. cooperation. International Journal of Production Research, 58 (12), 3618-3649.
Colicchia, C., Dallaria, F., & Melacini, M. (2010). Increasing supply chain resilience in a global sourcing context. Production Planning & Control, 21 (7), 680–694.
Carvalho, H., Barroso, A., Machado, V., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation. Computers & Industrial Engineering, 62 (1), 329–341.
Datta, P., Christopher, M., & Allen, P. (2007). Agent-based modelling of complex production/distribution systems to improve resilience. International Journal of Logistics Research and Applications, 10 (3), 187–203.
Diehl, M., Bock, H. G., Diedam, H., & Wieber, P.B. (2006). Fast direct multiple shooting algorithms for optimal robot control. Fast Motions in Biomechanics and Robotics, 65–93.
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: An analysis and recent literature. International Journal of Production Research, 56 (2), 414-430.
Falasca, M., Zobel, C., & Cook, D. (2008). A decision support framework to assess supply chain resilience. In Proceedings of the 5th International ISCRAM Conference, Washington, DC, USA, 596–605.
Fattahi, M., Govindan, K., & Maihami, R. (2020). Stochastic optimization of disruption-driven supply chain network design with a new resilience metric. International Journal of Production Economics, 230, 107755.
Fiksel, J., Polyviou, M., Croxton, K. L., & Pettit, K. J. (2015). From risk to resilience: Learning to deal with disruption. MIT Sloan Management Review, Winter issue.
Gurnani, H., Mehrotra, A., & Ray, S. (2012). Supply chain disruptions: Theory and practice of managing risk. London: Springer.
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.
Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K., & Al Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213 ,124-137.
Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics,180, 68–87.
Ivanov, D. (2017). Simulation-based ripple effect modelling in the supply chain. International Journal of Production Research, 55 (7), 2083–2101.
Ivanov, D., Sokolov, B., & Pavlov, A. (2013). Dual problem formulation and its application to optimal re-design of an integrated production–distribution network with structure dynamics and ripple effect considerations. International Journal of Production Research, 51 (18), 5386–5403.
Ivanov, D., & Sokolov, B. (2012). Structure dynamics control approach to supply chain planning and adaptation. International Journal of Production Research , 50 (21), 6133–6149.
Ivanov, D., Sokolov, B., Pavlov, A., Dolgui, A., & Pavlov, D. (2016). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies. Transportation Research Part E: Logistics and Transportation Review, 90, 7–24.
Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55 (20), 6158-6174.
Jabarzadeh, 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.
Kamalahmadi, M., & Mellat Parast, M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116–133.
Khalili, S. M., Jolai, F., & Torabi, S. A. (2017). Integrated production–distribution planning in two-echelon systems: A resilience view. International Journal of Production Research, 55 (4), 1040–1064.
Khamseh, A., Teimoury, E., & Shahanaghi, K. (2020). A new dynamic optimisation model for operational supply chain recovery, International Journal of Production Research, doi: 10.1080/00207543.2020.1842937.
Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics, doi: 10.1016/j.ijpe.2020.107882.
Margolis, J. T., Sullivan, K. M., Mason, S. J., & Maganotti, M. (2018). A multi-objective optimization model for designing resilient supply chain networks. International Journal of Production Economics, 204, 174–185.
Melnyk, S. A., Closs, D. J., Griffis, S. E., Zobel, C.W., & Macdonald, J. R. (2014). Understanding  supply chain resilience. Supply Chain Management Review, 18 (1), 34–41.
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.
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.
Olivares-Aguila, J., & ElMaraghy, W. (2020). System dynamics modelling for supply chain disruptions. International Journal of Production Research, doi: 10.1080/00207543.2020.1725171.
Pavlov, A., Ivanov, D., Dolgui, A., & Sokolov, B. (2018). Hybrid fuzzy-probabilistic approach to supply chain resilience assessment. IEEE Transactions on Engineering Management, 65 (2), 303–315.
Rao, A. )2009(. A survey of numerical methods for optimal control. Advances in the Astronautical Sciences, 135, 497–528.
Ratick, S., Meacham, B., & Aoyama, Y. (2008). Locating backup facilities to enhance supply chain disaster resilience. Growth and Change, 39 (4), 642–666.
Rezapour, S., Farahani, R., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259 (3), 1017–1035.
Sawik, T. 2020. Supply chain disruption management using stochastic mixed integer programming. 2nd  ed. New York: Springer.
Schmitt, A. J. & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139, 22–32.
Sethi, S. P., & Thompson, G. L. (2000). Optimal Control Theory: Applications to Management Science and Economics. 2nd ed. Berlin: Springer.
Spiegler, V., Naim, M., & Wikner, J. (2012). A control engineering approach to the assessment of supply chain resilience. International Journal of Production Research, 50 (21), 6162–6187.
Sawik, T. (2017). A portfolio approach to supply chain disruption management. International Journal of Production Research, 55(7): 1970-1991.
Simchi-Levi, D., Schmidt, W., & Wei, Y. (2014). From superstorms to factory fires. Harvard Business Review, 92, 96–101.
Tierney, K., & Bruneau, M. (2007). Conceptualizing and measuring resilience: A key to disaster loss reduction. TR News, 250, 14-17.
Torabi, S. A., Baghersad, M., & Mansouri, S.A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22–48.
Xu, M., Wang, X., & Zhao, L. (2014). Predicted supply chain resilience based on structural evolution against random supply disruptions. International Journal of Systems Science: Operations & Logistics, 1 (2), 105–117.
Wang, X., Herty, M., & Zhao, L. (2016). Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective. International Transactions in Operational Research, 23 (4), 775–796.