Abstract
In planning for a large-scale disaster, potential relief centers to accommodate evacuees need to be identified. The quantities of emergency commodities are prepared and stocked at relief centers in advance for possible disasters. In the event of a disaster, due to the different disaster severities and uncertain environment, some relief centers inevitably have surplus commodities, whereas some relief centers are still unmet. To use any surplus commodities effectively, a multi-commodity rebalancing process is necessary to rebalance the commodities among relief centers. However, various uncertainties make the multi-commodity rebalancing process extremely challenging, including uncertain demand and transportation-network availability. By recognizing those practical uncertainties, a bi-level stochastic mixed-integer nonlinear programming model is proposed to formulate this multi-commodity rebalancing problem. The upper-level objective is to minimize the total dissatisfaction level, which is measured by the expected total weighted unsatisfied demand, and the lower-level objective is to minimize the expected total transportation time. Finally, a case study on the Great Sichuan Earthquake in China is implemented; their results show that the proposed model facilitates effective decision-making in the practice of multi-commodity rebalancing.
Similar content being viewed by others
Change history
07 July 2021
Editor’s Note: Readers are alerted that ownership of data reported in this manuscript is currently under dispute. Appropriate editorial action will be taken once this matter is resolved.
References
Alizadeh, S., Marcotte, P., & Savard, G. (2013). Two-stage stochastic bilevel programming over a transportation network. Transportation Research Part B: Methodological, 58, 92–105.
Arnette, A. N., & Zobel, C. W. (2019). A risk-based approach to improving disaster relief asset pre-positioning. Production and Operations Management, 28(2), 457–478.
Bai, X. (2016). Two-stage multiobjective optimization for emergency supplies allocation problem under integrated uncertainty. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/2823835.
Balcik, B., Silvestri, S., Rancourt, M. È., & Laporte, G. (2019). Collaborative prepositioning network design for regional disaster response. Production and Operations Management., 28(10), 2431–2455.
Besiou, M., & Van Wassenhove, L. N. (2019). Humanitarian operations: A world of opportunity for relevant and impactful research. Manufacturing and Service Operations Management, 2019, 1–11.
Bracken, J., & McGill, J. T. (1973). Mathematical programs with optimization problems in the constraints. Operations Research, 21(1), 37–44.
Camacho-Vallejo, J.-F., González-Rodríguez, E., Almaguer, F.-J., & González-Ramírez, R. G. (2015). A bi-level optimization model for aid distribution after the occurrence of a disaster. Journal of Cleaner Production, 105, 134–145.
Cao, C., Li, C., Yang, Q., Liu, Y., & Qu, T. (2018). A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of Cleaner Production, 174, 1422–1435.
Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4–13.
Chen, L.-H., & Chen, H.-H. (2013). Considering decision decentralizations to solve bi-level multi-objective decision-making problems: A fuzzy approach. Applied Mathematical Modelling, 37(10–11), 6884–6898.
Chen, Y., Tadikamalla, P.R., Shang, J., & Song, Y. (2017). Supply allocation: Bi-level programming and differential evolution algorithm for natural disaster relief. Cluster Computing. https://doi.org/10.1007/s10586-017-1366-6.
Clark, A., & Culkin, B. (2013). A network transshipment model for planning humanitarian relief operations after a natural disaster, decision aid models for disaster management and emergencies. Atlantis Computational Intelligence Systems, 7, 233–257.
Dubey, R., Gunasekaran, A., & Papadopoulos, T. (2019). Disaster relief operations: past, present and future. Annals of Operations Research, 283(1–2), 1–8.
Elci, O., & Noyan, N. (2018). A chance-constrained two-stage stochastic programming model for humanitarian relief network design. Transportation Research Part B-Methodological, 108, 55–83.
Erbeyoğlu, G., & Bilge, Ü. (2020). A robust disaster preparedness model for effective and fair disaster response. European Journal of Operational Research, 280(2), 479–494.
Falasca, M., & Zobel, C. W. (2011). A two-stage procurement model for humanitarian relief supply chains. Journal of Humanitarian Logistics and Supply Chain Management, 1(2), 151–169.
Gao, X. (2019). A stochastic optimization model for commodity rebalancing under traffic congestion in disaster response. In IFIP international conference on advances in production management systems (pp. 91–99). Springer: New York.
Gao, X., & Lee, G.M. (2018a). A stochastic programming model for multi-commodity redistribution planning in disaster response. In IFIP international conference on advances in production management systems, (pp. 67–78). Springer: New York.
Gao, X., & Lee, G.M. (2018b). A two-stage stochastic programming model for commodity redistribution under uncertainty in disaster response. In Proceedings of international conference on computers and industrial engineering, CIE.
Gao, X., Nayeem, M.K., & Hezam, I.M. (2019). A robust two-stage transit-based evacuation model for large-scale disaster response. Measurement, 145, 713–723.
Goldschmidt, K. H., & Kumar, S. (2017). Reducing the cost of humanitarian operations through disaster preparation and preparedness. Annals of Operations Research, 283(1–2), 1139–1152.
Grass, E., & Fischer, K. (2016). Two-stage stochastic programming in disaster management: A literature survey. Surveys in Operations Research and Management Science, 21(2), 85–100.
Guha-Sapir, D., Vos, F., Below, F., & Ponserre, S. (2012). Annual disaster statistical review 2011: The numbers and trends. In Centre for research on the epidemiology of disasters (CRED).
Gutjahr, W. J., & Dzubur, N. (2016). Bi-objective bilevel optimization of distribution center locations considering user equilibria. Transportation Research Part E: Logistics and Transportation Review, 85, 1–22.
Haghi, M., Ghomi, S. M. T. F., & Jolai, F. (2017). Developing a robust multi-objective model for pre/post disaster times under uncertainty in demand and resource. Journal of Cleaner Production, 154, 188–202.
Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L. N., & Aros-Vera, F. (2013). On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management, 31(5), 262–280.
Hong, X., Lejeune, M. A., & Noyan, N. (2015). Stochastic network design for disaster preparedness. IIE Transactions, 47(4), 329–357.
Hu, C., Liu, X., & Hua, Y. (2016). A bi-objective robust model for emergency resource allocation under uncertainty. International Journal of Production Research, 54(24), 7421–7438.
Huang, K., Jiang, Y., Yuan, Y., & Zhao, L. (2015). Modeling multiple humanitarian objectives in emergency response to large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 75, 1–17.
Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Transportation Research Part E: Logistics and Transportation Review, 48(1), 2–18.
Jia, H., Ordóñez, F., & Dessouky, M. M. (2007). Solution approaches for facility location of medical supplies for large-scale emergencies. Computers and Industrial Engineering, 52(2), 257–276.
Kongsomsaksakul, S., Yang, C., & Chen, A. (2005). Shelter location-allocation model for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 6, 4237–4252.
Li, C., Zhang, F., Cao, C., Liu, Y., & Qu, T. (2019). Organizational coordination in sustainable humanitarian supply chain: An evolutionary game approach. Journal of Cleaner Production, 219, 291–303.
Lin, Y.-H., Batta, R., Rogerson, P. A., Blatt, A., & Flanigan, M. (2011). A logistics model for emergency supply of critical items in the aftermath of a disaster. Socio-Economic Planning Sciences, 45(4), 132–145.
Mahootchi, M., & Golmohammadi, S. (2018). Developing a new stochastic model considering bi-directional relations in a natural disaster: A possible earthquake in Tehran (the Capital of Islamic Republic of Iran). Annals of Operations Research, 269(1–2), 439–473.
Mete, H. O., & Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1), 76–84.
Mohammadi, R., Ghomi, S. F., & Jolai, F. (2016). Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm. Applied Mathematical Modelling, 40(9), 5183–5199.
Moreno, A., Alem, D., Ferreira, D., & Clark, A. (2018). An effective two-stage stochastic multi-trip location-transportation model with social concerns in relief supply chains. European Journal of Operational Research, 269(3), 1050–1071.
Murali, P., Ordóñez, F., & Dessouky, M. M. (2012). Facility location under demand uncertainty: Response to a large-scale bio-terror attack. Socio-Economic Planning Sciences, 46(1), 78–87.
Nagurney, A., Flores, E. A., & Soylu, C. (2016). A Generalized Nash Equilibrium network model for post-disaster humanitarian relief. Transportation Research Part E: Logistics and Transportation Review, 95, 1–18.
Nagurney, A., Masoumi, A. H., & Yu, M. (2015). An integrated disaster relief supply chain network model with time targets and demand uncertainty. In Regional science matters Springer: New York.
Ni, W., Shu, J., & Song, M. (2018). Location and emergency inventory pre-positioning for disaster response operations: Min–Max robust model and a case study of Yushu Earthquake. Production and Operations Management, 27(1), 160–183.
Noyan, N. (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Computers and Operations Research, 39(3), 541–559.
Noyan, N., Balcik, B., & Atakan, S. (2015). A stochastic optimization model for designing last mile relief networks. Transportation Science, 50(3), 1092–1113.
Paul, J. A., & Zhang, M. (2019). Supply location and transportation planning for hurricanes: A two-stage stochastic programming framework. European Journal of Operational Research, 274(1), 108–125.
Rath, S., Gendreau, M., & Gutjahr, W. J. (2016). Bi-objective stochastic programming models for determining depot locations in disaster relief operations. International Transactions in Operational Research, 23(6), 997–1023.
Rawls, C. G., & Turnquist, M. A. (2012). Pre-positioning and dynamic delivery planning for short-term response following a natural disaster. Socio-Economic Planning Sciences, 46(1), 46–54.
Rennemo, S. J., Rø, K. F., Hvattum, L. M., & Tirado, G. (2014). A three-stage stochastic facility routing model for disaster response planning. Transportation Research Part E: Logistics and Transportation Review, 62, 116–135.
Rezaei-Malek, M., & Tavakkoli-Moghaddam, R. (2014). Robust humanitarian relief logistics network planning. Uncertain Supply Chain Management, 2(2), 73–96.
Rivera-Royero, D., Galindo, G., & Yie-Pinedo, R. (2016). A dynamic model for disaster response considering prioritized demand points. Socio-Economic Planning Sciences, 55, 59–75.
Rodríguez-Espíndola, O., Albores, P., & Brewster, C. (2018). Dynamic formulation for humanitarian response operations incorporating multiple organisations. International Journal of Production Economics, 204, 83–98.
Ronke, P. (2018). Natural catastrophes and man-made disasters in 2017: A year of record-breaking losses. Sigma, 2018(1), 1–59.
Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M., & Heydari, M. (2018). An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: a case study. Annals of Operations Research, 283(1–2), 643–677.
Safaei, A. S., Farsad, S., & Paydar, M. M. (2018). Robust bi-level optimization of relief logistics operations. Applied Mathematical Modelling, 56, 359–380.
Sheu, J.-B. (2010). Dynamic relief-demand management for emergency logistics operations under large-scale disasters. Transportation Research Part E: Logistics and Transportation Review, 46(1), 1–17.
Sheu, J.-B., & Pan, C. (2015). Relief supply collaboration for emergency logistics responses to large-scale disasters. Transportmetrica A: Transport Science, 11(3), 210–242.
Starr, M. K., & Van Wassenhove, L. N. (2014). Introduction to the special issue on humanitarian operations and crisis management. Production and Operations Management, 23(6), 925–937.
Tavana, M., Abtahi, A.-R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21–37.
Tomasini, R. M., & Van Wassenhove, L. N. (2004). A framework to unravel, prioritize and coordinate vulnerability and complexity factors affecting a humanitarian response operation, INSEAD, Faculty and Research (pp. 1–15).
Van Wassenhove, L. N. (2006). Humanitarian aid logistics: Supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475–489.
Wang, H., Du, L., & Ma, S. (2014). Multi-objective open location-routing model with split delivery for optimized relief distribution in post-earthquake. Transportation Research Part E: Logistics and Transportation Review, 69, 160–179.
Wang, Y., & Sun, B. (2018). A multiobjective allocation model for emergency resources that balance efficiency and fairness. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/7943498.
Yilmaz, H., & Kabak, Ö. (2016). A multiple objective mathematical program to determine locations of disaster response distribution centers. IFAC-PapersOnLine, 49(12), 520–525.
Yu, L., Zhang, C., Yang, H., & Miao, L. (2018). Novel methods for resource allocation in humanitarian logistics considering human suffering. Computers and Industrial Engineering, 119, 1–20.
Zhou, Y., Liu, J., Zhang, Y., & Gan, X. (2017). A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review, 99, 77–95.
Acknowledgements
This research was partially supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (NRF-2017R1A2B4004169) and the China–Korea cooperation program managed by the National Natural Science Foundation of China and the NRF (NRF-2018K2A9A2A06019662).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gao, X. A bi-level stochastic optimization model for multi-commodity rebalancing under uncertainty in disaster response. Ann Oper Res 319, 115–148 (2022). https://doi.org/10.1007/s10479-019-03506-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10479-019-03506-6