Algorithm 4 G-ERMPInput Set of vehicles V = Ci riSet of scenarios ξ1 P ← ∅2 Si ← ∅ ∀i ∈ V3 σi ← 0 ∀i ∈ V4 Γ ← ∅5 for each ε ∈ ξ do6 ˜S ˜P ←GD-ERMPVε7for each i ∈ ˜P do8for each j ∈ ˜Si do9wji ← wji +

Initialize an empty set P to store the selected vehicles.
Initialize an empty set Si for each vehicle i, to store the scenarios in which it is selected.
Initialize a variable σi=0 for each vehicle i, to keep track of the number of times it has been selected in the scenarios.
Initialize an empty set Γ to store the set of vehicles that have been selected in the current iteration.
For each scenario ε in ξ, run the GD-ERMP algorithm to obtain the set of selected vehicles and the corresponding scenario set.
Update the set P with the new selected vehicles.
For each newly selected vehicle i in P, update the scenario set Si of all previously selected vehicles j in Si, by incrementing the weight wji of the edge between i and j.
For each vehicle i in V\P, update its in-degree indegi as the sum of the weights of all incoming edges in Si.
While |Γ| is less than the total number of vehicles V, do steps 10-29.
Find the vehicle j with the highest in-degree indegj among the vehicles in V\P.
Add j to the set P and Γ.
For each vehicle i in P, check if j is in its scenario set Si. If yes, remove j from Si.
For each vehicle k in V\Γ, check if the weight wki of the edge between k and j is positive and if k is available to be selected in the current scenario S. If yes, add k to Si and update its σk and Γ if necessary.
For each vehicle g in V\Γ, check if the weight wgj of the edge between j and g is positive and if g is available to be selected in the current scenario Sj. If yes, add g to Sj and update its σg and Γ if necessary.
Output the set of selected vehicles P and their corresponding scenario sets Si