Dual Optimization of Pick Routing and Tote Fill Rates
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Solution Overview
Problem
Conventional order picking systems in e-commerce facilities often lead to inefficiencies as pickers are assigned orders randomly, resulting in some pickers handling large or heavy orders, which increases the number of trips to the order assembly station, thereby delaying the order picking process.
Innovation Solution
The system performs dual optimization of pick lists for each picker by considering both item proximity constraints and tote value constraints, ensuring that each picker minimizes returns to the order assembly station and processes orders efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If orders are assigned randomly to pickers, then order assignment is simple and quick, but some pickers handle large or heavy orders which increases trips to the order assembly station and delays the picking process
Solution Approach 1:
The system performs preliminary optimization of pick lists before assignment to pickers. By pre-calculating optimal order batches considering item proximity constraints and tote value constraints, the system prepares balanced pick lists that minimize trips to the order assembly station, thereby improving picking speed without causing delays
Solution Approach 2:
The system dynamically optimizes pick lists by considering real-time constraints such as item proximity and tote capacity. The optimization process adapts to different order compositions and picker capabilities, creating dynamic batch assignments that reduce unnecessary trips while maintaining simple and quick assignment execution
2Productivity
If pick lists are optimized considering item proximity and tote constraints, then picking efficiency improves, but the optimization process becomes more complex
Solution Approach 1:
The optimization process is segmented into distinct constraints: item proximity constraints and tote value constraints. By dividing the optimization problem into these manageable segments, the system can process each constraint separately and combine results, improving picking efficiency while keeping the overall process tractable and not excessively complex
3Loss of time
If pickers minimize returns to the order assembly station, then processing time is reduced, but tote capacity utilization must be precisely optimized
Solution Approach 1:
The system changes the parameters used for order batching by incorporating tote value constraints (weight, volume, size) alongside item proximity constraints. By adjusting these parameters in the optimization process, the system achieves precise tote capacity utilization that minimizes returns to the order assembly station and reduces overall processing time
Data Source
AI summary
Methods and systems and computer-readable media are provided for dual optimization of pick walk and tote fill rates in order picking. Embodiments provide improved order picking speed and quality by optimizing pick routing with consideration of both proximity constraints and tote value constraints. Tote value constraints can include constraints on carrying capacity, volume, size in a particular dimension, or weight capacity.