Pick Tour Generating Subsystem for Warehouse Order Fulfillment
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Solution Overview
Problem
Conventional item picking and packaging systems in e-commerce and home shopping industries are inefficient and require significant capital investments, failing to optimize the picking and packaging process.
Innovation Solution
An order fulfillment system that determines whether customer orders warrant bulk picking or pick tours, using a pick tour generating subsystem to create optimized pick routes and plans, incorporating a value sorted tree map to efficiently manage mobile carts and tote arrangements within a warehouse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional manual picking and packaging techniques are used, then capital investment is reduced, but productivity and efficiency are low
Solution Approach 1:
The system segments the picking process into discrete pick tours, each targeting specific items at specific locations. Pick agents execute individual pick tours rather than handling entire orders at once, breaking down the complex fulfillment process into manageable, optimized segments that can be efficiently executed and tracked
Solution Approach 2:
The system performs preliminary actions by pre-determining optimal pick routes and sequences before agents begin picking. The route optimization algorithm calculates the most efficient path through warehouse locations in advance, and pick tours are planned ahead of time with predetermined item locations and sequences, eliminating the need for real-time decision-making during the picking process
2Productivity
If automated picking stations and packing machines are introduced, then productivity increases, but capital investment and device complexity increase significantly
Solution Approach 1:
The system dynamically adjusts pick tours and routes based on real-time order data and warehouse conditions. Rather than using fixed, rigid automation, the system adapts pick sequences and paths dynamically while maintaining operational efficiency, allowing flexibility without requiring complex automated infrastructure
Solution Approach 2:
The system introduces a software-based intermediary layer (the route optimization algorithm and pick tour management system) that coordinates human pick agents with warehouse operations. This software intermediary performs the complex optimization functions that would otherwise require expensive automated hardware, bridging the gap between manual labor and intelligent decision-making
3Loss of time
If conventional item picking systems are used, then device complexity is reduced, but loss of time and productivity are high
Solution Approach 1:
The system performs preliminary actions by pre-determining optimal pick routes and sequences before agents begin picking. The route optimization algorithm calculates the most efficient path through warehouse locations in advance, and pick tours are planned ahead of time with predetermined item locations and sequences, eliminating the need for real-time decision-making during the picking process
Solution Approach 2:
The system replaces mechanical route-determination methods with an algorithmic optimization system. Instead of using fixed routes or human intuition to determine picking sequences, the system uses computational algorithms to calculate and prescribe optimal paths, substituting mechanical/simple processes with intelligent software-based decision-making that reduces time loss
Data Source
AI summary
The customer order fulfillment system includes an order collection unit for collecting information associated with a plurality of customer orders from a plurality of customers and generating customer order data that includes data associated with each of the plurality of customer orders and the plurality of customers. Each of the plurality of customer order includes one or more items associated therewith. The system also includes an order generating unit for receiving the customer order data from the order collection unit and generating in response thereto consolidated order fulfillment data, and a pick tour generating subsystem for receiving the consolidated order fulfillment data from the order generating unit and in response thereto generating pick tour instructions associated with a pick tour from the consolidated order fulfillment data.