Input: The graph \(G\), the parameter \(K\), the limit number \(E_{\text{max}\text{~}}\), and a starting solution \(s\) (could be empty)
Output: A solution for the current time step
1: Compute \(KG\);
2: while time is available do
3:       Initialize the backbone graph \(BG\) by removing all arcs of \(KG;\)
4:       while \(BG\) has less than \(E_{\max}\) arcs do
5:             For each customer \(c\), generate uniformly a pick-up time \(t_{c}\) from \(I_{c}\) or from \(I_{c}^{s}\) (is the time window where the solution \(s\) is propagated in \(I_{c}\) );
6:             Solve maxFlow on \(KG\) with the \(t_{c}\) pick-up times of the customers;
7:             Add the optimal arcs of the solution to the graph \(BG;\)
8:       end while
9:       Solve MIPmaxflow on \(BG;\)
10:     Update the solution \(s;\)
11: end while