Warehouse Item Scheduling Using Parallel Shipment Prediction Models
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Solution Overview
Problem
Current manual allocation methods for scheduling items in warehouses often lead to inefficiencies, particularly when there are either too many or too few items, affecting timeliness and resource allocation, and fail to accurately meet user demands.
Innovation Solution
A method and apparatus that utilize a shipment prediction model with parallel sub-models and a weighted sub-model to determine total shipment volume information, integrating historical data to schedule items based on predicted demand and current inventory, ensuring accurate and timely allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual allocation is used to schedule items to warehouses, then operational simplicity is maintained, but scheduling accuracy and timeliness deteriorate
Solution Approach 1:
The patent replaces the manual mechanical allocation system with an automated computer-based prediction system. The system uses historical shipment data, inventory information, and external factors (weather, holidays, promotions) to automatically generate optimized scheduling plans, substituting human manual operations with algorithmic processing that provides both automation and accuracy.
Solution Approach 2:
The system enables self-service scheduling by automatically analyzing data and generating allocation plans without requiring manual intervention. The prediction model autonomously processes historical data, current inventory status, and external factors to produce scheduling recommendations, allowing the system to serve itself rather than relying on manual allocation.
2Loss of time
If items are scheduled from other warehouses when local inventory is insufficient, then shipping timeliness is improved, but resource allocation efficiency deteriorates due to negative impact on other warehouses
Solution Approach 1:
The system performs preliminary actions by predicting future shipment volumes and inventory needs before actual shipping occurs. By analyzing historical data and external factors in advance, the system proactively schedules item transfers between warehouses to prevent stockouts, ensuring timeliness while optimizing overall resource allocation across the network rather than reactively transferring items when needed.
Solution Approach 2:
The prediction model serves multiple functions simultaneously: it predicts local shipment volumes, forecasts inventory requirements, optimizes inter-warehouse transfers, and balances overall network resource allocation. This multi-functional approach resolves the contradiction by coordinating shipments across warehouses in a unified manner that maintains timeliness while preventing negative impacts on other locations.
3Reliability
If more items are stored in the warehouse to ensure supply, then user demand satisfaction is improved, but inventory cost and resource allocation efficiency deteriorate
Solution Approach 1:
The system dynamically adjusts inventory parameters based on predicted shipment volumes and demand patterns. By changing inventory levels according to forecasted needs rather than maintaining static high stock, the system ensures demand satisfaction while optimizing inventory quantities. The prediction model continuously updates recommended inventory levels based on historical data, seasonal patterns, and external factors.
Solution Approach 2:
The patent implements dynamic inventory management where stock levels are continuously adjusted based on real-time data and predictions. Rather than maintaining fixed high inventory levels, the system adapts inventory quantities to match predicted demand, ensuring reliability when needed while minimizing excess stock. This dynamic approach allows the system to respond flexibly to changing conditions and optimize the balance between availability and inventory costs.
Data Source
AI summary
A method and apparatus for scheduling an item, and a computer-readable storage medium are provided. The method can include acquiring a first time sequence corresponding to a target item in a target warehouse, the first time sequence including shipment volume information of the target item corresponding to each unit time within a first historical period. The method can further include determining, according to the first time sequence and a target period to be predicted, total shipment volume information of the target item within a target period through a shipment prediction model, the shipment prediction model including a plurality of parallel first time sequence sub-models and a weighted sub-model which is connected to an output of each of the first time sequence sub-models, and scheduling the target item in the target warehouse according to the total shipment volume information and current inventory information of the target item in the target warehouse.