Warehouse Picking Station Allocation for Workload-Balanced Order Fulfillment
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Solution Overview
Problem
Existing warehouse picking systems face inefficiencies due to factors like storage structure, equipment workload, turnover rate, and order specifications, leading to resource waste and delivery delays, especially exacerbated by rising labor costs and the impact of the pandemic.
Innovation Solution
A warehouse picking system and method utilizing multiple cargo management stations, cargo picking and transportation equipment, and a management device with a processor and communication device to optimize order processing by calculating order scores, selecting target stations and boxes based on workload and product information, and determining efficient picking and transportation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional warehouse picking systems are used, then storage space and equipment are limited, but delivery time is delayed and resource waste occurs
Solution Approach 1:
The warehouse is divided into multiple cargo management stations, each responsible for specific cargo types. This segmentation allows parallel processing of different orders and cargo types, significantly improving picking efficiency and reducing delivery time delays.
Solution Approach 2:
The system pre-calculates order scores and determines optimal cargo management station assignments before actual picking operations. This preliminary planning optimizes resource allocation in advance, enabling faster order fulfillment and reducing delivery delays.
2Productivity
If more cargo management stations are added, then picking efficiency improves, but system complexity increases
Solution Approach 1:
Each cargo management station is designed with universal functionality to handle multiple cargo types and order configurations. The standardized multi-functional design allows the system to scale by adding stations without proportionally increasing operational complexity, as each station can adapt to different picking scenarios.
3Quantity of substance
If cargo picking boxes are optimized for storage capacity, then cargo volume increases, but picking equipment workload increases
Solution Approach 1:
The system dynamically assigns cargo picking boxes to orders based on real-time equipment workload conditions. When picking equipment is heavily loaded, the system automatically selects alternative cargo picking boxes that optimize the distribution of work, thereby managing equipment workload while maintaining high cargo storage capacity.
4Loss of time
If order score calculation is used to prioritize orders, then order completion time improves, but calculation complexity increases
Solution Approach 1:
The order score function uses a set of defined parameters (cargo management station adaptability, cargo picking box availability, equipment workload) that can be adjusted based on warehouse conditions. By changing these parameters dynamically, the system optimizes order completion time while keeping the calculation methodology systematic and manageable.
Data Source
AI summary
A warehouse picking system and a warehouse picking method are provided. The warehouse picking system includes multiple cargo management stations and a management device. The management device receives multiple orders including at least one cargo, calculates an order score of each order by using an order score function; selects at least one order as a target order according to the order scores so as to determine a target cargo management station adapted for processing the target order; selects a cargo picking box storing at least one cargo in the target order from the cargo picking boxes of the target cargo management station to serve as candidate cargo picking boxes; and determines a target cargo picking box adapted for picking the cargo in the target order according to a workload of cargo picking equipment, a workload of cargo transportation equipment, and a disposition of each candidate cargo picking box.