赛题介绍 Description

      随着不断增长的顾客需求和相继而来的成本压力，给消费互联网电子商务企业的运营提出了越来越大的挑战。供应链和仓储物流管理的创新在电子商务运营中的作用尤为突出。以京东为例，在全国建设的8个区域型配送中心1500+大小仓库中储存了超千万不同种类的商品，超过 5.88 亿的活跃用户，绝大多数订单需要满足211配送标准，即早上11点之前的订单当日送达，晚上11点之前的订单次日下午3点前送达。

      精确预测不同地区消费者对商品的需求，并在靠近消费者的仓库中提前储备这些商品，对于确保快速配送至关重要。同时，在确保配送效率的基础上，利用大数据分析和优化算法来减少运营成本，是企业长期发展的关键任务。

With the growing customer demand and the successive cost pressures, the operation of consumer Internet e-commerce enterprises has posed more and more challenges. Innovation in supply chain and warehousing logistics management plays a particularly prominent role in e-commerce operations. Taking JD.com as an example, more than 10 million different kinds of goods are stored in 1500+ warehouses in 8 regional distribution centers built across the country, with more than 588 million active users, and the vast majority of orders need to meet the 211 delivery standard, that is, orders before 11 am will be delivered on the same day, and orders before 11 pm will be delivered before 3 pm the next day.

Accurately forecasting consumer demand for goods in different regions and stocking them in warehouses close to consumers in advance is essential to ensure fast delivery. At the same time, on the basis of ensuring distribution efficiency, the use of big data analysis and optimization algorithms to reduce operating costs is a key task for the long-term development of enterprises.

比赛任务 Contest tasks

      基于简化后的京东区域仓与前置仓的两级仓库网络，根据历史数据，通过运筹优化，机器学习，深度学习等算法进行多层库存布局优化，降低库存与缺货成本，准确地预测不同地区对商品的需求，将顾客需要的货品配置到最合适的仓库，以最高效的运营效率保障高时效。

Based on the simplified two-level warehouse network of JD's regional warehouse and front-end warehouse, according to historical data, multi-layer inventory layout optimization is carried out through operation research optimization, machine learning, deep learning and other algorithms, so as to reduce inventory and out-of-stock costs, accurately predict the demand for goods in different regions, and allocate the goods required by customers to the most suitable warehouse, so as to ensure high timeliness with the most efficient operational efficiency.