This study focused on order-picking system optimization by optimizing the joint performance problem between order batching and the pick order assignment in order-picking activities using a simulation approach. Order batching is used to classify the orders into some groups based on their order similarity. It will allow more orders processed concurrently to have a high affinity, allowing more SKUs to be picked from one pod and fewer pods needed. This study will also optimize the order-to-station assignment by considering the association between the new order and the currently active order being processed in the station instead of randomly assigning it. The objective is to provide a combined solution to deal with a dynamic environment and maximize the total throughput by increasing the pile-on value and minimizing the number of pods needed to fulfill the whole order. A simulation-based technique is used for solving the problem with two different scenarios: random as a baseline scenario and similarity-based order grouping and assignment as the second scenario.
The result shows that the proposed model significantly lowers the number of pods needed to fulfill the order. The proposed model outperforms and is competitive enough compared with the baseline scenario. Using similarity-based order batching and the order assignment model can decrease the average number of pods needed by 40%. It is in line with the pod’s utilization percentage, which means dividing the number of pods needed by the total number of pods in the warehouse. Furthermore, putting an order with the highest similarity into the same batch has proven that it can increase the pile-on value of the system since it allows the simulation to achieve almost 70% better pile-on compared with the baseline scenario. The proposed model also sequences the order assignment to the station based on the similarity, allowing a higher chance of similar orders being processed concurrently. This strategy helped the system achieve higher throughput of around 3% by assigning similar orders concurrently within the batch and considering the active order in the picking station before assigning the batch. This order assignment strategy increases the possibility and ensures that every order and/or batch of orders always goes to the picking station with the most similar order. Then, it will help to maximize the pile-on, minimize the pod movement, and decrease the service time for each order fulfillment.

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