This thesis studies a Sequential Resource Allocation (SRA) problem that is faced by food distribution of a nonprofit organization (e.g. food banks). Different from commercial operations’ objective aimed at maximizing profit, the primary objective arising in nonprofit organizations is to fairly satisfy the demand of recipients. Rising demand and limited resource increase the importance of effective food allocation operations that maximize equity and resource utilization at the same time. In the context of food bank operations, we consider the problem of collecting an uncertain quantities of donation and allocating them sequentially to meet customers’ demands that are uncertain until arriving at the customer’s location. A SRA model is formulated that can be used to design an optimal visiting route; it focus on equity maximization and waste reduction. Without considering travel cost/time restriction, our work solves the problem by developing a new objective function to minimize the filling rate (i.e., the ratio of the allocation quantity to observed demand) gap among agencies. An experimentation is designed to evaluate and analyze the performance of the algorithm, and the proposed method yield better solutions in terms of waste reduction. Furthermore, by using adaptive large neighborhood search (ALNS), we extend the model to include travel cost into consideration to find a near-optimal visiting route. A case study with larger scale is also performed that shows the algorithm obtains high-quality solutions in terms of equity and efficiency (travel costs).