Input: The graph \(G\), the parameter \(K\), 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:       while exploration time is available do
4:             For each customer \(c\), generate uniformly a pick-up time \(t_{c} \in I_{c}\) or \(t_{c} \in I_{c}^{s};\)
5:             Solve maxFlow on \(KG\) with \(t_{c}\) pick-up times of customer;
6:             Add the arc profits of the solution to features \(X\) and \(\left( t_{c} \right)\) to \(y;\)
7:       end while
8:       Train the RNN model \(m\) using \(X\) and \(y;\)
9:       Generate times by \(\left. \ \mathbf{t} = m\left( \left\lbrack R_{e_{1}},\ldots,R_{|E|} \right) \right\rbrack^{'} \right);\)
10:     Solve maxFlow with times \(\mathbf{t};\)
11:     Update the solution \(s;\)
12: end while