The packing process is a critical post-harvesting activity in table grape industry. Workers in packing stations are hired under seasonal contracts because of product seasonality and operations labor intensity. Seasonal workers, however, are usually characterized by inconsistent performance, high turnover and experience variation which leads to low productivity and high waste. Few mathematical models were used for evaluating fresh products packing operations, but in a deterministic nature which hinders the complexity and dynamics of the business processes. Hence, a hybrid Discrete Event Simulation (DES) and Agent-Based Modelling (ABM) are employed to evaluate a set of seasonal recruiting policies in a large grape packing station. The paper aims to investigate the impact of workers experience on packing operations efficiency. The model outcomes demonstrate the improvement in operations efficiency and total running cost (about 20% savings) that can be achieved when applying optimal recruiting policies that reduce labors variations.