1505. Improving the optimization of the Online Order Batching by combining the Maximum Throughput Time and Picking Time
Invited abstract in session TB-15: Heuristic Search 2, stream Combinatorial Optimization.
Tuesday, 10:30-12:00Room: Esther Simpson 1.08
Authors (first author is the speaker)
| 1. | Sergio Gil-Borras
|
| Sistemas Informaticos, Universidad Politecnica de Madrid | |
| 2. | Eduardo G. Pardo
|
| Informática y EstadÃstica, Universidad Rey Juan Carlos |
Abstract
The Online Order Batching Problem has gained prominence in recent years due to the rapid expansion of e-commerce platforms, which seek to streamline every step of their supply chain. In the context of warehouse management, optimizing the picking process using a batching policy is a critical area for cost reduction.
In this study, we focus on enhancing order batching by considering two distinct objective functions simultaneously: minimizing the picking time and minimizing the maximum throughput time. To achieve this, we propose a weighted composite objective function that balances these two goals that will be used as a guiding function. We conduct three experiments to evaluate different configurations. The first one shows that using a combined objective function yields to better results in minimizing the maximum throughput time than using just the original function as guidance. The second configuration targets improvements in the picking time, and the third one seeks to enhance both objectives simultaneously.
Our methodology relies on GRASP+VND, an approach previously validated with strong outcomes for OOBP, to generate batches of orders. We then apply the S-Shape heuristic to determine efficient picking routes due to its simplicity and rapid computation. To further refine the results, we test two time-window policies, which influence when new orders enter the batching process. Our findings demonstrate that these strategies can significantly improve warehouse operations.
Keywords
- Combinatorial Optimization
- Supply Chain Management
- Warehouse Design, Planning, and Control
Status: accepted
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