Interchange Node Selection for Delivery Routing

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Solution Overview

Problem

Existing methods for vehicle route guidance in delivering alimentary combination identifiers, such as food and parcels, suffer from inaccuracy in path selection and neglect the optimization potential of secondary locations for delivery infrastructure, leading to inefficient delivery processes.

Innovation Solution

A machine-learning simulation system determines physical transfer interchange nodes by generating candidate locations using a Monte Carlo simulation and machine-learning processes to minimize transfer time for physical transfer apparatuses, selecting the most efficient interchange nodes and displaying them for use in pairing apparatuses with items.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If existing vehicle route guidance methods are used for delivery, then basic path selection is provided, but accuracy in predictions is insufficient and secondary locations are neglected

Engineering Contradiction:
Improveprediction accuracyVSAvoiddelivery process efficiency
Core Design Contradiction:
Measurement precisionVSProductivity

Solution Approach 1:

The delivery system is segmented into multiple components: primary delivery locations and secondary interchange nodes. The route is divided into segments that can be optimized independently, allowing for more precise predictions at each stage while maintaining overall delivery efficiency.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

Secondary interchange nodes are introduced as intermediary locations between origin and destination. These nodes act as mediators that enable more accurate predictions by breaking down the delivery path into manageable segments, thereby improving both prediction accuracy and delivery efficiency.

Inventive Principle:
Principle #24Intermediary (Mediator)

2Productivity

If traditional route guidance is used, then simple path selection is achieved, but inaccuracy in predictions used to support further computations occurs

Engineering Contradiction:
Improvepath selection efficiencyVSAvoidprediction accuracy
Core Design Contradiction:
ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-identifying secondary interchange nodes and calculating optimal routes to these nodes before final delivery. This preliminary route planning to intermediate points enables more accurate predictions for subsequent computational steps while maintaining efficient path selection.

Inventive Principle:
Principle #10Preliminary action

3Device complexity

If secondary locations are neglected, then delivery infrastructure is simplified, but optimization potential for delivery processes is lost

Engineering Contradiction:
Improvedelivery infrastructure complexityVSAvoiddelivery process optimization
Core Design Contradiction:
Device complexityVSProductivity

Solution Approach 1:

The delivery infrastructure is segmented into primary locations and secondary interchange nodes. This segmentation allows the system to maintain relatively simple primary infrastructure while adding optimized secondary nodes that provide the necessary complexity for process optimization without overwhelming system complexity.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

Secondary interchange nodes are strategically placed at specific locations where they provide localized optimization benefits. Rather than uniformly complicating the entire infrastructure, the system applies enhanced complexity only at critical intermediate points where optimization potential exists, thereby improving delivery processes without excessive overall complexity.

Inventive Principle:
Principle #3Local quality

Data Source

PatentUS11308422B2Method of and system for determining physical transfer interchange nodes
Publication Date: 2022.04.19 KPN INNOVATIONS LLC
  • US11308422B2 patent drawing
  • US11308422B2 patent drawing
  • US11308422B2 patent drawing

AI summary

A system for determining physical transfer interchange nodes using machine-learning simulation, the system comprising a computing device, wherein the computing device is configured to receive a plurality of alimentary combination identifiers and a plurality of physical transfer apparatus data. Computing device may select an interchange node for the plurality of physical transfer apparatuses, wherein selecting further comprises generating candidate interchange nodes, wherein generating further comprises using an interchange node machine-learning process to generate candidate interchange nodes, each candidate including a location and a time, calculating, using the candidate interchange nodes and the interchange node machine-learning process, a transfer path time for each physical transfer apparatus to each candidate interchange node, and selecting a candidate interchange node that minimizes the transfer time for each the plurality of physical transfer apparatuses. Computing device may display the selected interchange node to a plurality of physical transfer apparatuses.