Dynamic Order Batching for Retail Picker Travel and Tote Fill
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Solution Overview
Problem
Current in-store picking systems are inefficient due to static batch order constraints, limited tote capacity, and suboptimal routing, leading to increased distance traveled by pickers and reduced tote fill rates.
Innovation Solution
A computing device optimizes order batching by separating orders by load number and due times, using an optimization algorithm that considers distance and volume to generate rebatched orders, which are then sorted by sequence numbers for efficient picking, thereby minimizing picker travel distance and maximizing tote fill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static batch order constraints are used with fixed tote capacity, then order fulfillment can be completed, but picker travel distance increases and tote fill rates decrease
Solution Approach 1:
The system transitions from static batch order constraints to dynamic optimization that continuously adjusts order batching and tote allocation based on real-time parameters including item distance, volume, and picker capacity. The optimization algorithm dynamically rebatches orders to minimize travel distance while maximizing tote fill rates.
Solution Approach 2:
The system changes multiple parameters simultaneously including order batch composition, tote allocation, and picking sequence. By optimizing parameters such as batch size, item grouping by proximity, and volumetric utilization, the system improves picker performance while reducing travel distance.
2Productivity
If more items are batched into fewer totes, then tote fill rate improves, but item selection flexibility decreases
Solution Approach 1:
The system performs preliminary optimization calculations to determine the optimal combination of items for each tote before picking begins. By pre-calculating the best item selection based on volume, distance, and order requirements, the system achieves high tote fill rates while maintaining the flexibility to adapt to different order compositions.
Solution Approach 2:
The system replaces manual item selection and physical tote filling with an optimization algorithm that calculates optimal item combinations. This computational approach substitutes the mechanical process of manual item selection, enabling high tote fill rates while maintaining adaptability through algorithmic flexibility.
Data Source
AI summary
A method including receiving a plurality of orders comprising one or more items, wherein the server computing device is configured to communicate with a plurality of user devices of a plurality of users associated with a plurality of retail stores. The method also can include batching the plurality of orders into different commodities, and generating a plurality of rebatched orders according to an optimization algorithm. The method additionally can include sorting the one or more items within the plurality of rebatched orders by sequence numbers based on at least a respective volume of each respective one of a number of containers. The method further can include transmitting the one or more items within the plurality of rebatched orders to the plurality of user devices for filling the each respective one of the number of containers with the one or more items within the plurality of rebatched orders. Other embodiments are disclosed herein.