Faculty of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran


BACKGROUND AND OBJECTIVES: After having struck in a major natural disaster like an earthquake, different organizations run about to decrease losses. The lack of accurate demand information is a common problem that all emergency response organizations have to encounter such a crisis. Evaluation of the City disaster level is a mean to feed this information to the disaster response operations. The objective of this research is eschedule a group of experts to assess relief demand. These evaluation teams need to be scheduled to minimize the evaluation time.
METHODS: This paper aims to formulate the routing and scheduling of the assessment teams so that real demand information for savings and rescue would be available as soon as possible. The simulated annealing algorithm is used to solve the scheduling problem.
FINDING: two cost functions, sum of arrival time and max completion time, were evaluated. The latest is found to perform better in evaluation of the teams performance.
CONCLUSION:The performance of the approach is tested on several randomly generated networks and synthesized demand data. The results show a 13 % improvement in the total completion time of operation in comparison with previous approaches.


Main Subjects

Albris, K.; Lauta, K.C.; Raju, E., (2020). Strengthening governance for disaster prevention: The enhancing risk management capabilities guidelines. Int. J. Disast. Risk Re., 47: 101647 (8 pages).

Altay, N.; Green, W.G., (2006). OR/MS research in disaster operations management. Eur. J. Oper. Res., 175(1): 475-493 (19 pages).

Aviv, Y., (2003). A time-series framework for supply-chain inventory management. Oper. Res., 51(2): 210-227 (18 pages).

Bai, X., (2016). Two-Stage multiobjective optimization for emergency supplies allocation problem under integrated uncertainty. Math. Prob. Eng., 2823835: 1-13 (13 pages).

Barbarosoǧlu, G.; Arda, Y., (2004). A two-stage stochastic programming framework for transportation planning in disaster response. J. Oper. Res. Soc., 55(1): 43-53 (11 pages).

Blitch, J.G., (1996). Artificial intelligence technologies for robot assisted urban search and rescue. Expert Sys. Appl., 11(2): 109-124 (16 pages).

Campbell, A.M.; Vandenbussche, D.; Hermann, W., (2008). Routing for relief efforts. Transp. Sci., 42: 127-145 (19 pages).

Chen, L.; Miller-Hooks, E., (2012). Optimal team deployment in urban search and rescue. Transport. Res. Bart B: Methodological, 46(8): 984-999 (16 pages).

Edrisi, A.; Askari, M., (2019). Probabilistic budget allocation for improving efficiency of transportation

networks in pre-and post-disaster phases. Int. J. Disast. Risk Re., 39: 101113 (9 pages).

Edrisi, A.; Askari, M., (2020). Multi-objective location model of earthquake shelters. Int. J. Hum. Capital in Urban Manage., 5(1): 19-26 (8 pages).

Edrissi, A.; Poorzahedy, H.; Nassiri, H.; Nourinejad, M., (2013). A multi-agent optimization formulation of earthquake disaster prevention and management. Eur. J. Oper. Res., 229(1): 261-275 (15 pages).

Fiedrich, F.; Gehbauer, F.; Rickers, U., (2000). Optimized resource allocation for emergency response after earthquake disasters. Safety Sci., 35(1-3): 41-57 (17 pages).

Galindo, G.; Batta, R., (2013). Review of recent developments in OR/MS research in disaster operations management. Eur. J. Oper. Res., 230(2): 201-211 (11 pages).

Gonzalez-R, P.L.; Canca, D.; Andrade-Pineda, J.L.; Calle, M.; Leon-Blanco, J.M., (2020). Truck-drone team logistics: A heuristic approach to multi-drop route planning. Transport. Res. Part C-Emer. Technol., 114: 657-680 (24 pages).

Huang, M.; Smilowitz, K.R.; Balcik, B., (2013). A continuous approximation approach for assessment routing in disaster relief. Transport. Res. Part B-Meth., 50: 20-41 (22 pages).

Konstantinidou, M.A.; Kepaptsoglou, K. L.; Karlaftis, M.G.; Stathopoulos, A., (2015). Joint Evacuation and Emergency Traffic Management Model with Consideration of Emergency Response Needs. Transp. Res. Record., 2532(1): 107-117 (11 pages).

Li, D.; Zhou, H., (2018). Robust Optimization for Vehicle Routing Problem Under Uncertainty in Disaster Response. 15th International Conference on Service Systems and Service Management (ICSSSM), Hangzhou (5 pages).

Luis, E.; Dolinskaya, I.S.; Smilowitz, K.R., (2012). Disaster relief routing: Integrating research and practice. Socio. Econ. Plan. Sci., 46(1): 88-97 (10 pages).

Malik, M.; Cruickshank, H., (2016).  Disaster management in Pakistan. Proc. I. Civ. Eng. Munic., 169(2): 85-99 (15 pages).

Nadi, A.; Edrisi, A., (2017). Adaptive multi-agent relief assessment and emergency response. Int. J. Disast. Risk. Reduct., 24: 12-23 (12 pages).

Özdamar, L.; Ekinci, E.; Küçükyazici, B., (2004). Emergency logistics planning in natural disasters. Ann. Oper. Res., 129: 217-245 (29 pages).

Rodriguez-Espindola, O.; Alborez, P.; Brewster, C., (2018). Dynamic formulation for humanitarian response operations incorporating multiple organisations. Int. J. Prod. Econ., 204: 83-98 (16 pages).

Sarma, D.; Das, A.; Bera, U.K., (2020). Uncertain demand estimation with optimization of time and cost using Facebook disaster map in emergency relief operation. Appl. Soft Comput., 87: 105992.

Sheu, J.B., (2007). An emergency logistics distribution approach for quick response to urgent relief demand in disasters. Transport. Res. Part E, Logist. Transport. Rev., 43(6): 687-709 (23 pages).

Sun, B.; Ma, W.; Zhao, H., (2013). A fuzzy rough set approach to emergency material demand prediction over two universes. Appl. Math. Model., 37(10-11): 7062-7070 (9 pages).

Tang, J.; Zhu, K.; Guo, H.; Liao, C.; Zhang, S., (2017). Simulation optimization of search and rescue in disaster relief based on distributed auction mechanism. Algorithms, 10(4): 125 (17 pages).

Wex, F.; Schryen, G.; Feuerriegel, S.; Neumann, D., (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units. Eur. J. Oper. Res, 235(3): 697-708 (12 pages).

Wu, Y.; Chen, S., (2019). Resilience modeling of traffic network in post-earthquake emergency medical response considering interactions between infrastructures, people, and hazard. Sustainable Resilient Infrastruct., 4(2): 82-97 (16 pages).

Yi, W.; Kumar, A., (2007). Ant colony optimization for disaster relief operations. Transport. Res. E-Log., 43(6): 660-672 (13 pages).

Zhu, M.; Zhang, X.; Luo, H.; Wang, G.; Zhang, B., (2020). optimization dubins path of multiple UAVs for post-earthquake rapid-assessment. Appl. Sci., 10(4): 1388 (24 pages).


International Journal of Human Capital in Urban Management (IJHCUM) welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in IJHCUM should be sent to the editorial office of IJHCUM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.

[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.

[3] Letters can be no more than 300 words in length.

[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.

[5] Anonymous letters will not be considered.

[6] Letter writers must include their city and state of residence or work.

[7] Letters will be edited for clarity and length.