AGV Container Positioning Using Order Likelihood Prediction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing solutions for organizing inventory items in storage facilities are inefficient, as they rely on order history and are unable to determine optimal storage positions or release customer orders effectively, leading to frequent transportation of containers and inefficiencies in order fulfillment.

Innovation Solution

A method and system that determine container utilization likelihoods and optimal positions based on order likelihoods, using automated guided vehicles (AGVs) to transport containers to these positions, thereby optimizing storage and order fulfillment processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If containers are transported frequently to and from picking stations based on existing order history approaches, then order fulfillment can be performed, but transportation efficiency deteriorates and time is lost

Engineering Contradiction:

Improveorder fulfillment efficiencyVSAvoidtransportation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting which containers will be needed in the future and transporting them to picking stations in advance, before actual customer orders are placed. This proactive approach reduces the need for frequent reactive transportation and improves order fulfillment efficiency while minimizing transportation time losses.

Inventive Principle:

Principle #10Preliminary action

2Speed

If containers are kept at picking stations to fulfill incoming orders, then order fulfillment speed improves, but device complexity increases due to container management

Engineering Contradiction:

Improveorder fulfillment speedVSAvoidcontainer management complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The system implements self-service by using predictive models to automatically determine which containers should be positioned at picking stations and when they should be released or replaced. This automated decision-making reduces the complexity of manual container management while maintaining fast order fulfillment speeds.

Inventive Principle:

Principle #25Self-service

3Productivity

If predictive models are used to determine container positions, then transportation efficiency improves, but measurement precision requirements increase for order likelihood predictions

Engineering Contradiction:

Improvetransportation efficiencyVSAvoidorder likelihood prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system applies parameter changes by using multiple predictive models with different time windows (e.g., 1-hour, 24-hour, 7-day predictions) to evaluate container utilization likelihood from multiple temporal perspectives. This multi-parameter approach improves transportation efficiency while managing prediction accuracy requirements through diversified temporal analysis.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11030573B2Automated guided vehicle control and organizing inventory items using predictive models for slow item types

Publication Date: 2021.06.08 STAPLES INC

AI summary

In an example embodiment, a method determines a container containing one or more items associated with one or more item types, the container located at a current position in a storage facility; determines one or more order likelihoods of the one or more item types contained in the container; determines a container utilization likelihood of the container based on the one or more order likelihoods of the one or more item types contained in the container; determines an optimal position for the container in the storage facility based on the container utilization likelihood of the container, the optimal position being different from the current position of the container; and instructing an automated guided vehicle (AGV) to transport the container from the current position of the container to the optimal position of the container in the storage facility.