This paper examines the problem of daily operations of same-day delivery with crowdshipping and store fulfillment (SDD-CSF). We aim to close the last-mile delivery gap between local stores and customers. SDD-CSF makes order fulfillment plan from two aspects: order souring decision and delivery method selection in order to minimize the cost associated with the order fulfillment plan. We adopt the new concept of last-mile delivery from local stores using crowdsourced shipping, which includes two specific delivery methods based on the distinct characteristics of crowdsourced shippers: Information Sharing Driver (ISDs) and Occasional Drivers (ODs). We devise a dynamic programming model for order fulfillment in a rolling horizon framework, which later is mathematically approximated into a mixed integer linear programming model. The model considers both currently received orders and the predicted future demand to make order assignment decision that minimizes the immediate delivery cost plus the resulting future expected cost. It repeatedly solves the model following the timeline in order to construct an optimal fulfillment plan from local stores. With the help of the rolling horizon structure, we also introduce a feedback control system to cope with the inaccurate forecast of future demand. Finally, we prove that under perfect information, the proposed formulation can converge to the global optimum.