Evaluación de estrategias de picking mediante simulación basada en eventos

Abstract

This research evaluated picking strategies through discrete-event simulation with the aim of identifying the most efficient approach to optimize warehouse performance. Six operational scenarios were analyzed, combining different inventory organization methods (disorganized, rack-organized, and ABC-classified) with two types of resources: human operators and automated guided vehicles (AGVs). The results showed that inventory organization has a significant impact on key performance indicators (KPIs) such as cycle time, travel distance, and idle time. Rackorganized scenarios, especially when combined with partial automation, demonstrated substantial improvements compared to the disorganized baseline. Additionally, ABCclassified environments also yielded competitive results. To complement the univariate analysis, a Principal Component Analysis (PCA) was applied, allowing for a multivariate assessment of efficiency. This integrative approach revealed that Scenario 6 (ABC organization with AGVs) achieved the best overall performance, followed by Scenario 5 (rack organization with AGVs) and Scenario 3 (ABC organization with human operators). The findings support the conclusion that hybrid strategies, which combine efficient organization with partial automation, provide a favorable balance between operational performance and feasibility. The choice of a picking strategy should be aligned with the specific characteristics of the warehouse, order profile, and logistical objectives. Moreover, simulation is highlighted as a valuable decision-support tool, enabling the evaluation of strategic alternatives without disrupting real operations and promoting a more efficient, flexible, and sustainable warehouse managemen