Online-to-offline (O2O) food delivery service refers to an emerging modern business model that enables customers to order foods from local restaurants via an online platform, and then receive and enjoy them offline after the delivery, offered by couriers. Such service, discussed in this article, specifies that a customer can order from multiple restaurants in a single order and choose for them to be delivered together or separately, whereas the commonly discussed mode only permits placing an order in one restaurant at once. In this service, one crucial issue is how to dispatch these orders to couriers for offline delivery. For this, we propose a new three-stage order dispatch scheme, namely, pseudo-assign first, re-route second, and courier selection last, aiming to deliver the orders in time and balance the couriers’ workload. Due to the dynamism and uncertainty inherently involved in this issue, we also take responsiveness to future demands and robustness into consideration when making the dispatch. Compared with existing approaches, the new one significantly balances the couriers’ workload and, meanwhile, keeps good performance in delay rate, making the decisions more practical. Furthermore, this study analyzes the influence of customers’ preference for order splitting and number of couriers on the efficiency of the distribution system, thereby interesting managerial insights for O2O food delivery are revealed.

In supply chain management practices, supplier selection (SS) is a critical strategic planning activity that usually constitutes an ex ante decision made under uncertainty, whereas order allocation (OA) represents a subsequent operational decision determined ex post, contingent upon both the selected suppliers and actual operational conditions observed during the execution phase—specifically, the realized scenarios of uncertain circumstances. The practical performance of an SS decision inherently depends on its subsequent OA outcomes, while the OA decision itself is constrained by the preceding SS choices. Nevertheless, existing studies typically tackle the SS and OA problems separately or formulate them within a single-stage programming model, failing to adequately capture their sequential interdependence and the impact of OA on SS evaluation. To address this gap, this study develops novel two-stage bi-objective stochastic programming models in which the first-stage SS decisions are evaluated based on two key criteria—total cost and purchasing value—both of which depend on the second-stage OA decisions in response to realized operational scenarios. The stochastic performance of a given SS scheme, arising from adaptive OA decisions under uncertainty, is measured by expected value and conditional value-at-risk. An integrated approach combining weighted-satisfaction sum, linearization, Monte Carlo simulation, and genetic algorithm is developed to solve the models. Computational experiments demonstrate the effectiveness of the proposed methodology and reveal the influence of objective preferences and risk-aversion levels on the optimal supplier selection.