3D Bin Packing Algorithms APIs to optimize shipments

Scalable API access to state of the art of machine learning and genetic algorithms for 3D bin container packing service for ecommerce, warehousing and logistic. We save you thousands of hours of development to minimize cost and surface area, volume, weight of packed container.

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The bin packing problem (BPP) is a classic and important optimization problem in logistics and warehousing systems. Out proprietary N dimensional item clustering algorithm is easy and ready to onboard

Ecommerce shipments packing

Awesome things come in small packages.

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Cargo containers loadouts

Software to simplify planning and fitting boxes

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Truck load planning

Boxify and load planning software with online calculator

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Machine learning for optimal packing solution

3D genetic algorithm and reinforcement deep neural network trained over millions of real word simulations and learned how optimize space efficiency and capacity. Better clustering and sequencing are also available.

Simply put, the problem is how to use the space most economically when placing a certain number of items in containers (or "bins").

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2 boxes

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7 boxes

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20 boxes

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60 boxes

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Single layer pack

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Flat ordered

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Fit all space

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60 boxes

3D packing Features

We provide world-class infrastructure for developers so you can spend your time building app.

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Intuitive Multi-container packing

Single or Multi 3d container packing algorithm as a service.

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Scalable and secure api

Connect your app immediately with our instant access APIs. We support JSON-RPC over HTTPS

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Easy and efficient item clustering

We spent thousands of human and compute hours to perfect the solution

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Support

Our 24/7 team of experts is ready to handle all network changes and upgrades so you can focus on building your applications.

Simple 3D bin packaging API


import requests

data = {
    "containers": [
        {"width": 50, "height": 15, "depth": 15, "max_weight": 100, "tag": "Truck A"},
        {"width": 13, "height": 8, "depth": 6, "max_weight": 200, "tag": "Truck B"},
    ],
    "items": [
        {"width": 5, "height": 3, "depth": 2, "qt": 20, "weight": 10, "tag": "Goods"},
        {"width": 3, "height": 3, "depth": 3, "qt": 30, "weight": 12.5, "tag": "TV"},
    ],
    "options": {"blueprint": True, "beta": True},
}
requests.post("https://api.robo-packing.ml/containers/gen-A1?api_key=ak_45111910264f4e68a08b18434ab4e4f", json=data)


from dataclasses import dataclass
from typing import List


@dataclass
class Container:
    width: int
    height: int
    depth: int
    max_weight: int
    tag: str


@dataclass
class Item:
    width: int
    height: int
    depth: int
    qt: int
    weight: float
    tag: str


@dataclass
class Options:
    blueprint: bool
    beta: bool


@dataclass
class Request:
    containers: List[Container]
    items: List[Item]
    options: Options



//    payload, err := UnmarshalPayload(bytes)
//    bytes, err = Payload.Marshal()

package main

import "encoding/json"

func UnmarshalPayload(data []byte) (Payload, error) {
	var r Payload
	err := json.Unmarshal(data, &r)
	return r, err
}

func (r *Payload) Marshal() ([]byte, error) {
	return json.Marshal(r)
}

type Payload struct {
	Containers []Container `json:"containers"`
	Items      []Item      `json:"items"`
	Options    Options     `json:"options"`
}

type Container struct {
	Width     int64  `json:"width"`
	Height    int64  `json:"height"`
	Depth     int64  `json:"depth"`
	MaxWeight int64  `json:"max_weight"`
	Tag       string `json:"tag"`
}

type Item struct {
	Width  int64   `json:"width"`
	Height int64   `json:"height"`
	Depth  int64   `json:"depth"`
	Qt     int64   `json:"qt"`
	Weight float64 `json:"weight"`
	Tag    string  `json:"tag"`
}

type Options struct {
	Blueprint bool `json:"blueprint"`
	Beta      bool `json:"beta"`
}
        

Free 3D bin packing calculator

Pricing Plans

Explorer

Free

Early access

Business

$299 per 10k calls

Buy Now

Enterprise

Fixed price

Contact

Support 3 months 12 months Custom
On Premise
API calls/month 100 100k 10M +
Compute cluster size 1 shared unit 10-125 units 10-10000 units
Custom recalibration

Roadmap

2020 Features under development FAQ?

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3 new algorithms

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Better efficiency

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On premise docker container

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Better visualization

eliza@robo-packing.ml

Questions? Please reach out and I'll be happy to assist you.