Packing planning device, packing system, packing planning method and program

JP2026101506APending Publication Date: 2026-06-22NEC CORP +1

Patent Information

Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
NEC CORP
Filing Date
2024-12-10
Publication Date
2026-06-22

AI Technical Summary

Technical Problem

Existing technologies face challenges in automating the packing of items into containers with high efficiency and minimizing the burden of training machine learning models for different container dimensions.

Method used

A packing planning device that includes scaling parameter calculation, planning problem calculation, stacking planning, and rescaling to convert between target and standardized container dimensions, using machine learning models trained on standardized containers.

Benefits of technology

Reduces the burden of training machine learning models and enhances packing efficiency by standardizing container dimensions, allowing for effective packing plans without retraining.

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Abstract

This reduces the burden of training machine learning models to plan the packing of items into containers. [Solution] The packing planning device comprises: a scaling parameter calculation means that calculates scaling parameter values, which are coefficient values ​​for converting the dimensions of a target container model, which is a container to be packed with goods, to the dimensions of a standardized container model, which is a model of a container having certain specific dimensions; a planning problem calculation means that converts the dimensions of a target item model, which is a goods to be packed into the target container, using the scaling parameter values; a stacking planning means that generates a plan for packing the dimensionally converted target item model into a standardized container model using a machine learning model trained on a standardized container model; and a rescaling means that converts the generated plan into a plan for packing the target item model into a target container model using the scaling parameter values.
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