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Comment by NicoJuicy | Hacker News Reader

Similar to this, since i'm in ecommerce. Does anyone know of "load plan software" for packages.

Given the height, width and depth of every product and the package dimensions available?

It's interesting that the lack of this data was one of the failure points for Target's $4.4 billion USD Canadian expansion that ended in failure. It's a really interesting story [0]

> A team assigned to investigate the problem discovered an astounding number of errors. Product dimensions would be in inches, not centimetres or entered in the wrong order: width by height by length, instead of, say, length by width by height. Sometimes the wrong currency was used. Item descriptions were vague. Important information was missing. There were myriad typos. “You name it, it was wrong,” says a former employee. “It was a disaster.”

> Getting the details from suppliers largely fell on the young merchandising assistants. In the industry, information from vendors is notoriously unreliable, but merchandising assistants were often not experienced enough to challenge vendors on the accuracy of the product information they provided.

> The investigative team estimated information in the system was accurate about 30% of the time.

[0] Source: http://www.macleans.ca/economy/business/what-really-happened...

Quality of data is probably one of the saddest reasons why systems can be unreliable and bad decisions can be easily made in many organizations that aren't technically oriented. It's the biggest reason why I think data analysis logic and/or data modeling should somehow be incorporated into public high school curriculum plans. Systems will work better when the people who use them have a better appreciation for caring about the quality of their data and also the nature of data relationships in databases. This is completely separate from the concepts taught in programming classes, but of course related.

Better understanding of the nature of data -> better data -> more useful systems -> better business decisions -> better business performance. I see too many people get frustrated and make poor decisions because they are unable to comprehend the nature of data. Productivity would soar if people understood how to model and take care of data. It's only one aspect of a complex issue, of course. Good UI, system uptime reliability, and so many other things also matter for whether an organization gets everything it really needs from a system.

Thank you for posting the story. I hadn't heard about it previously. Having spent a couple years at various businesses, getting accurate data and understanding exactly what you're looking at is more of a pain than I would have ever imagined.

There was a blogpost [1] a while back from a guy who runs a candy subscription business. He goes into some approaches and issues with this and also mentions a python package called pyShipping [2] which has an existing implementation using some heuristics to speed up the calculations.

1. https://www.candyjapan.com/behind-the-scenes/algorithmic-fit... 2. https://github.com/hudora/pyShipping/blob/master/pyshipping/...

I've built something like this for a client that manages shipping containers (like those in the article). There are a number of applications on the market which deal with this problem, but the pisk/packing bits tend to be part of much larger systems (ERP, WMS, and other fulfillment solutions) rather than standalone and those solutions can be a bit pricey. This is why we decided to build a solution into an existing enterprise package rather than buy something. I think the problem you're asking about is so narrow, the motivation for developing something that stands on its own is not really there... certainly on a commercial basis.

It's called The Knapsack Problem - and optimizing it is NP hard

https://en.wikipedia.org/wiki/Knapsack_problem

Solving the knapsack problem for practical instances is actually really easy. (That's a general pattern with NP hard problem: most of them are easy in practice.)

But the problem described is closer to two- or three-dimensional bin packing (or scheduling in general) than to the knapsack problem. See eg http://www.research.ibm.com/people/n/nikhil/papers/Bansal-pa...

Another thing to look at is the "Cutting stock" problem (https://en.wikipedia.org/wiki/Cutting_stock_problem).

Knapsack is only about weight, while this is about (most likely) packing 3D cuboids. But that problem is NP-hard, too ;)

All shipping containers are an identical size - it is a weight problem.

The original discussion about ships was, but this comment thread was about packing (retail packaged) products into a shipping carton.

I struggled with this for many years, trying to get accurate shipping quotes for furniture and other items that get charged by 'volumetric weight'. I got stuck at the combinatorial explosion of currencies, location 'zones', taxes, product setup data inconsistencies and what happens if not all items can be shipped together.

I spoke to a specialist developer that had been in the game longer than myself and she said 'nope, it can't be done that easily in real world situations' and proceeded to go all computer-science on me. If you are in ecommerce you might know of Karen from WebShopApps. If anyone can get you a best solution it is her team.

One of my manufacturing Customers uses software from this company: http://www.topseng.com/

It's often integrated into application specific software - for example, I worked on metal center management software which performed load and route planning for steel warehouse delivery trailers. To another poster's point, it's a twist on 3D bin packing with lots of custom heuristic possibilities as well as an obvious application for a genetic solver.

we're doing this, and call it case pack optimization: https://www.logivations.com/en/solutions/3d/behaelterportfol...

(disclosure: I work for that company)

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