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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 N ∪ {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 t?̃?il s The assessment time at node I under scenario s by team l t?̃?ijl s The assessment time at arc (i,j) under scenario s by team l Tmaxl s The maximum time that team l is allowed to evaluate under scenario s Cl s Transportation cost for team l per unit of distance under scenario s dmaxk s The maximum distance that team k is allowed to traverse under scenario s dmaxh s The maximum distance that team h is allowed to traverse under scenario s dij Distance from node i ∈ N to node j ∈ N d0i Distance from origin node to i ∈ N BudGT s Total transportation budget of the Governmental team under scenario s BudRT s Total transportation budget of the Red Crescent team under scenario s αic s The probability that node i has the characteristic c under scenario s αijr s The probability that arc (i,j) has the characteristic r under scenario s pi s The importance of node i ∈ N under scenario s qij s The importance of arc (i, j) ∈ A under scenario s Eij 1 if arc (i, j) ∈ A exists in the transportation network, and 0 otherwise Variables ui The sequence in which node i is visited x0il s 1 if node i is first node in the path of the team l under scenario s, and 0 otherwise xi0l s 1 if node i is last node in the path of team l under scenario s, and 0 otherwise xijl s 1 if team l visits node j ∈ N after node i ∈ N under scenario s, and 0 otherwise Ail s 1 if team l visits node i ∈ N under scenario s, and 0 otherwise zijl s 1 if team l visits arc (i, j) ∈ A under scenario s, and 0 otherwise *Corresponding Author Institutional Email: mrabani@ut.ac.ir (T. S. Danesh Alagheh Band) 2504 T. S. Danesh Alagheh Bnad et al. / IJE TRANSACTIONS C: Aspetcs Vol. 33, No. 12, (December 2020) 2503-2508

A Post-disaster Assessment Routing Multi-objective Problem under Uncertain Parameters