Solving an Emergency Resource Planning Problem with Deprivation Time by a Hybrid MetaHeuristic Algorithm

Document Type : 15th IIEC conference selected papers


1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

2 School of Industrial Engineering, College of Engineering, University of Tehran, P.O. Box: 11155-4563, Tehran, Iran



– Every year, natural disasters (e.g., floods and earthquakes) threaten people's lives and finances. To cope with the damage of natural disasters, emergency resources (e.g., rescue teams) must be planned efficiently. Therefore, designing a decision support model to allocate and schedule rescue teams is necessary for the response phase of disaster management. The literature review shows that social aspects of disaster management have less been addressed by researchers, whereas this phenomenon must be incorporated into decision-making processes. The lack of timely relief implies a loss in people's welfare, which leads to social costs called deprivation cost or time. This study proposes a multi-objective mixed-integer programming model to assign and schedule the rescue teams considering different rescuers' capabilities, fatigue effects, and deprivation time. Due to the NP-Hardness of the proposed model, a hybrid approach based on the Lp-metric method and meta-heuristic algorithms are applied to solve the given problem. The results show that the developed algorithm can obtain high-quality solutions in a reasonable time.


Bodaghi, B., Palaneeswaran, E., Shahparvari, S., & Mohammadi, M. (2020). Probabilistic allocation and scheduling of multiple resources for emergency operations; a Victorian bushfire case study. Computers, Environment and Urban Systems, 81, 101479.
Cotes, N., & Cantillo, V. (2019). Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences, 65, 89–100.
Cunha, V., Pessoa, L., Vellasco, M., Tanscheit, R., & Pacheco, M. A. (2018). A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem. In 2018 IEEE Congress on Evolutionary Computation (CEC) (pp. 1–6). IEEE.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39–43). IEEE.
Falasca, M., Zobel, C. W., & Fetter, G. M. (2009). An optimization model for humanitarian relief volunteer management. In Proceedings of the 6th International ISCRAM Conference.
Farahani, R. Z., Lotfi, M. M., Baghaian, A., Ruiz, R., & Rezapour, S. (2020). Mass casualty management in disaster scene: A systematic review of OR&MS research in humanitarian operations. European Journal of Operational Research.
Fiedrich, F., Gehbauer, F., & Rickers, U. (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Science, 35(1), 41–57.
GHASEMI, P., Khalili, D. K., HAFEZALKOTOB, A., & RAISSI, S. (2019). Presenting a Multi-objective Mathematical Model for Location, Allocation and Distribution of Relief Commodities under Uncertainty.
Glover, F., & Woolsey, E. (1974). Technical note—Converting the 0-1 polynomial programming problem to a 0-1 linear program. Operations Research, 22(1), 180–182.
Holland, J. (1975). Adaptation in artificial and natural systems. Ann Arbor: The University of Michigan Press.
Hu, C. L., Liu, X., & Hua, Y. K. (2016). A bi-objective robust model for emergency resource allocation under uncertainty. International Journal of Production Research, 54(24), 7421–7438.
karimi movahed,  kamran, Ghodrat Nama, A., & Zhang, Z. (2020). Optimal Relief Order Quantity Under Stochastic Demand and Lead Time. Journal of Quality Engineering and Production Optimization.
Kumar, J. S., & Zaveri, M. A. (2019). Resource Scheduling for Postdisaster Management in IoT Environment. Wireless Communications and Mobile Computing, 2019, Article ID 7802843, 2019.
Mir, M. S. S., & Rezaeian, J. (2016). A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines. Applied Soft Computing, 41, 488–504.
Mirzapour Al-E-Hashem, S. M. J., Malekly, H., & Aryanezhad, M. B. (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International Journal of Production Economics, 134(1), 28–42.
Mohamadi, S., Avakh Darestani, S., Vahdani, B., & Alinezhad, A. (2020). A Multi-Objective Optimization Model for Designing a Humanitarian Logistics Network under Service Sharing and Accident Risk Concerns under Uncertainty. Journal of Quality Engineering and Production Optimization.
Molladavoodi, H., Paydar, M. M., & Safaei, A. S. (2018). A disaster relief operations management model: a hybrid LP–GA approach. Neural Computing and Applications, 1–22.
Nayeri, S., Asadi-Gangraj, E., & Emami, S. (2018a). Metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect. Neural Computing and Applications, 1–21.
Nayeri, S., Asadi-Gangraj, E., & Emami, S. (2018b). Goal programming-based post-disaster decision making for allocation and scheduling the rescue units in natural disaster with time-window. International Journal of Industrial Engineering & Production Research, 29(1), 65–78.
Rauchecker, G., & Schryen, G. (2018). An Exact Branch-and-Price Algorithm for Scheduling Rescue Units during Disaster Response. European Journal of Operational Research.
Rolland, E., Patterson, R. A., Ward, K., & Dodin, B. (2010). Decision support for disaster management. Operations Management Research, 3(1–2), 68–79.
Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M., & Heydari, M. (2019). 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.
Santoso, A., Sutanto, R. A. P., Prayogo, D. N., & Parung, J. (2019). Development of fuzzy RUASP model-Grasp metaheuristics with time window: Case study of Mount Semeru eruption in East Java. In IOP Conference Series: Earth and Environmental Science (Vol. 235, p. 12081). IOP Publishing.
Shavarani, S. M., Golabi, M., & Vizvari, B. (2019). Assignment of Medical Staff to Operating Rooms in Disaster Preparedness: A Novel Stochastic Approach. IEEE Transactions on Engineering Management.
Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes.
Tamura, H., Yamamoto, K., Tomiyama, S., & Hatono, I. (2000). Modeling and analysis of decision making problem for mitigating natural disaster risks. European Journal of Operational Research, 122(2), 461–468.
Visheratin, A. A., Melnik, M., Nasonov, D., Butakov, N., & Boukhanovsky, A. V. (2017). Hybrid scheduling algorithm in early warning systems. Future Generation Computer Systems.
Wang, F., Pei, Z., Dong, L., & Ma, J. (2020). Emergency resource allocation for multi-period post-disaster using multi-objective cellular genetic algorithm. IEEE Access.
Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units. European Journal of Operational Research, 235(3), 697–708.
Wex, F., Schryen, G., & Neumann, D. (2011). Intelligent decision support for centralized coordination during emergency response.
Wex, F., Schryen, G., & Neumann, D. (2012). Operational emergency response under informational uncertainty: A fuzzy optimization model for scheduling and allocating rescue units.
Wex, F., Schryen, G., & Neumann, D. (2013). Decision modeling for assignments of collaborative rescue units during emergency response. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 166–175). IEEE.
Xu, N., Zhang, Q., Zhang, H., Hong, M., Akerkar, R., & Liang, Y. (2019). Global optimization for multi-stage construction of rescue units in disaster response. Sustainable Cities and Society, 51, 101768.
Zahedi, A., Kargari, M., & Kashan, A. H. (2020). Multi-objective decision-macking model for distribution planning of goods and routing of vehicles in emergency. International Journal of Disaster Risk Reduction, 101587.
Zhang, C., Liu, X., Jiang, Y. P., Fan, B., & Song, X. (2016). A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis. European Journal of Operational Research, 250(3), 855–864.
Zhang, S., Guo, H., Zhu, K., Yu, S., & Li, J. (2017). Multi-stage assignment optimization for emergency rescue teams in the disaster chain. Knowledge-Based Systems, 137, 123–137.