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dc.contributor.author | Kmet, Maksym![]() |
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dc.date.accessioned | 2024-02-14T11:42:45Z | |
dc.date.available | 2024-02-14T11:42:45Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Kmet, Maksym. Warehouse Layout Optimization for Order Picking in E-commerce Industry / Kmet, Maksym; Supervisor: Oleh Lukianykhin; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2022. – 35 p. | uk |
dc.identifier.uri | https://er.ucu.edu.ua/handle/1/4430 | |
dc.language.iso | en | uk |
dc.title | Warehouse Layout Optimization for Order Picking in E-commerce Industry | uk |
dc.type | Preprint | uk |
dc.status | Публікується вперше | uk |
dc.description.abstracten | This project focuses on the order picking process of a fulfillment center supplying products of e-commerce websites. The fulfillment center employs a manual picker system where pickers move on foot, collecting products from a batch of orders that were requested. Efficiency of an order picking process is defined by the time the pickers spend on collecting orders. This time can be separated into 2 components: orders picking time and orders sorting time. The goal of this project is to reduce order picking time by solving a storage location assignment problem (SLAP). The project consists of two main parts: picking time prediction and picking time optimisation by solving SLAP. As a result a Random Forest model was trained to predict orders picking time based on storage location of products. This model was used to estimate walking time change for new assigned product storage locations (new warehouse layout). New product storage locations were generated using suggested heuristic based on SHAP values obtained from the trained Random Forest model. The new layout satisfies required constraints and can be implemented in the corresponding real world warehouse, while being 23% more efficient in terms of order collection time. | uk |