IJE TRANSACTIONS C: Aspetcs Vol. 33, No. 12, (December 2020) 2503-2508 Please cite this article as: T. S. Danesh Alagheh Band, A. Aghsami, M. Rabbani, A Post-disaster Assessment Routing Multi-objective Problem under Uncertain Parameters, International Journal of Engineering, Transactions C: Aspects Vol. 33, No. 12, (2020), 2503-2508. International Journal of Engineering Journal Homepage: www.ije.ir A Post-disaster Assessment Routing Multi-objective Problem under Uncertain Parameters T. S. Danesh Alagheh Band* a,b , A. Aghsami a,c , M. Rabbani a a School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran b Arts et Métiers Paris Tech, Paris, France c School of Industrial Engineering, College of Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran PAPER INFO Paper history: Received 22 May 2020 Received in revised form 26 June 2020 Accepted 03 September 2020 Keywords: Post-disaster Assessment Multi-objective Grasshopper Optimization Algorithm A B S T RA C T Given that disasters are unavoidable, and many people are suffering from them each year, we should manage the emergencies and plan for them well to reduce mortality and financial losses. One of the measures that organizations must take after the disaster is the assessment of the conditions and needs of the people. We consider some characteristics for sites and roads and two teams for assessment as well as the uncertain assessment time to modeling. A multi-objective model is proposed in this study. The first objective function maximizes the gain from the assessment of areas and roads. The second and third objective functions maximize total coverage at damaged areas and roads. We use the LP-metric technique to solve small size problems in the GAMS software and the Grasshopper Optimization Algorithm (GOA) as a Meta-heuristic algorithm to solve a case study. Numerical results are presented to prove the credibility and efficiency of our model. doi: 10.5829/ije.2020.33.12c.10 NOMENCLATURE Sets N Set of all nodes (i,j ∈ N) N0  ∪ {0} , 0 is the origin node A Set of all arcs RT Set of Red Crescent Assessment Team (k ∈K) GT Set of Governmental Assessment Team (h∈H) L Set of all teams (l ∈ L) C Set of critical characteristics of nodes (c ∈ C) R Set of critical characteristics of arcs (r ∈ R) S Set of probability scenario (s ∈ S) Parameters     The assessment time at node I under scenario s by team l     The assessment time at arc (i,j) under scenario s by team l    The maximum time that team l is allowed to evaluate under scenario s    Transportation cost for team l per unit of distance under scenario s    The maximum distance that team k is allowed to traverse under scenario s  ℎ  The maximum distance that team h is allowed to traverse under scenario s   Distance from node ∈ to node ∈  0 Distance from origin node to ∈    Total transportation budget of the Governmental team under scenario s    Total transportation budget of the Red Crescent team under scenario s    The probability that node i has the characteristic c under scenario s    The probability that arc (i,j) has the characteristic r under scenario s    The importance of node ∈ under scenario s    The importance of arc (, ) ∈  under scenario s   1 if arc (, ) ∈  exists in the transportation network, and 0 otherwise Variables   The sequence in which node i is visited x 0il s 1 if node i is first node in the path of the team l under scenario s, and 0 otherwise x i0l s 1 if node i is last node in the path of team l under scenario s, and 0 otherwise x ijl s 1 if team l visits node ∈ after node ∈ under scenario s, and 0 otherwise A il s 1 if team l visits node ∈ under scenario s, and 0 otherwise z ijl s 1 if team l visits arc (, ) ∈  under scenario s, and 0 otherwise *Corresponding Author Institutional Email: mrabani@ut.ac.ir (T. S. Danesh Alagheh Band)