G-ERMP Algorithm: A Robust Vehicle Selection Approach

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'.