Autonomous Robot Fulfillment Modeling for Multiple Line Orders
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Solution Overview
Problem
Existing autonomous robotic mobile fulfilment systems are limited to fulfilling single line orders and impose restrictions on shelf height and weight, leading to reduced efficiency and elongated travel times when handling multiple line orders.
Innovation Solution
The development of autonomous guided vehicles that transport individual cases within a warehouse, combined with simulation and analytical modeling to analyze performance, allowing for efficient fulfillment of multiple line orders and optimizing warehouse layout and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous guided vehicles transport entire shelves of orders, then productivity is improved, but the height and weight of shelves are limited
Solution Approach 1:
The system segments the order fulfillment process by separating case retrieval from order assembly. Autonomous guided vehicles retrieve individual cases from storage locations and deliver them to workstations, where multiple cases are assembled into complete orders. This segmentation eliminates the need to transport entire shelves, removing weight and height limitations while maintaining high productivity.
2Productivity
If autonomous guided vehicles transport entire shelves, then single line orders are fulfilled efficiently, but multiple line orders require elongated travel times
Solution Approach 1:
The system performs preliminary actions by pre-positioning individual cases at various storage locations and having autonomous guided vehicles retrieve them independently. For multiple line orders, cases from different storage locations can be retrieved in parallel by multiple vehicles, and then assembled at the workstation. This eliminates the need for sequential shelf transport, significantly reducing travel time for multiple line orders while maintaining efficiency for single line orders.
3Measurement precision
If performance analysis methods are improved for accuracy, then measurement precision is enhanced, but computation time increases
Solution Approach 1:
The patent replaces traditional discrete event simulation (a computational mechanical system) with a closed-form analytical model. The analytical model uses mathematical equations to directly calculate performance metrics such as throughput and travel time, providing 90% accuracy while being 1000 times faster than simulation methods. This substitution eliminates the need for time-consuming computational iterations while maintaining high measurement precision.
Data Source
AI summary
Systems and methods for estimating performance of an autonomous robot system that is configured to fulfil multiple line orders is described herein. The autonomous robot system comprises a plurality of autonomous guided vehicles configured to transport one or more cases within an environment so as to fulfil the multiple line orders. In some variations, a method includes obtaining first input data, generating a simulation model based at least in part on the first input data, determining a travel duration for each of the plurality of autonomous guided vehicles based on an execution of the simulation model, generating an analytical model based at least in part on the travel duration, and estimating the performance of the autonomous robot system based on an execution of the analytical model. The first input data includes data associated with operation of each of the plurality of autonomous guided vehicles and data representing a layout of the environment.