Dynamic Driver Allocation Under Latent Demand Regimes: Indexability of a Partially Observed Markov Decision Process

Quick-commerce dark stores dispatch e-grocery orders within 15 to 30 minutes, so operators such as Getir, Glovo, and GoPuff must commit drivers before orders arrive.

Demand follows a latent regime that persists across hours, while unfulfilled orders spill forward as a compounding backlog.

This decision binds whether drivers are employed on fixed shifts or drawn from a gig platform whose incentives are set ahead of the hour, yet existing models do not learn the regime as orders arrive.

We formulate the single-store problem as a partially observable Markov decision process in which the firm infers the regime from realized orders.

We show that optimal staffing rises with backlog and prove the single-store problem is indexable, a property open for multi-action partially observed problems in general.

We then extend the framework to a driver pool shared across stores through a Lagrangian relaxation that decouples the network into per-store subproblems.

The result is a two-level allocation policy that prices the value of tracking demand in real time and ranks stores by a provably valid priority index.

On 2021 to 2022 data from the European firm SuperGlovo, which staffs full-time drivers to comply with Spain's Rider's Law, belief updating adds 10.9\%, about \$188K across 27 stores, over the same program with the belief held fixed.

The gain concentrates in the stores whose regimes are most persistent, observable before deployment.

Online allocation reduces to a table lookup and a ranked list with a cutoff price.