the courier or rider can perform the following recourse actions: pool capacity by making a detour towards a neighboring access hub with extra capacity or consign its load to a nearby third-party business (e.g. local shop) for a certain price agreed upon (uncapacitated recourse). Once volume requirements are observed, recourse actions are taken for each operational period $\tau$: capacity pools as a vector $P(\tau, \omega)=(P_a( \tau,\omega),\forall a\in A_{pool})$ where $A_{pool}$ is the set of arcs on which capacity can be pooled, and consignments as a vector $Z(\tau,\omega)=(Z_l(\tau,\omega),\forall l\in L)$. At any time $\tau$ in scenario $\omega$, the system can thus be represented as a state $S_t = S(t) \text{ s.t. } \tau \in T_{t}$ and an action $x_\tau = (R(\tau), P(\tau,\omega), Z(\tau,\omega)) \text{ s.t. } \tau \in T_{t}$, where $R(\tau)$ is the null vector except for $\tau = t, \forall t \in T$. Based on the optimisation framework proposed in [@powell2019unified], our stochastic optimization challenge for the access hub dynamic pooled capacity deployment problem can be formulated as follows: $$\label{opt challenge}
    \min_{x_\tau \in X(\tau)} \mathop{\mathbb{E}}_{\omega \in \Omega}\left \{\sum_{t \in T} \sum_{\tau \in T_t} C_\tau(S_t,x_\tau,\rho^P(\tau,\omega), \rho^D(\tau,\omega), D(\tau,\omega))|S_0 \right \}$$ where $X(\tau)$ is the set of feasible actions at time $\tau$, $S_0$ 