During the previous blogs, we introduced DeepPack, presented the technology behind it, and described its features and what differentiates it from manual load planning or traditional load planning tools.  In this blog, we will consider a common real-life scenario and show how DeepPack can save time and money for the user vs traditional manual methods.Company […]

        This November, the DeepPack team traveled to Amsterdam to showcase their innovative AI-powered bin-packing tool at Intermodal 2022. Back from a Covid-19 hiatus, the long-standing industry event welcomed visitors, exhibitors, and speakers from right across the spectrum of shipping and logistics services, all interested in learning about the latest innovations, technologies, […]

DeepPack Infrastructure The high-performance computing infrastructure behind DeepPack is an integral part of the product: it is running constantly in the background to quickly deliver accurate optimization results of very complex instances. As previously mentioned in part 3 of the blog, the user can select between two types of runs: instant and optimized. An in […]

In parts 1 and 2 of this blog, we discussed the technical capabilities of DeepPack and highlighted how Reinforcement Learning is well adapted to solve the load planning problem. In this blog, we will provide an outline of the panoply of features currently available.

We are pleased to confirm that the DeepPack Early Access Programme (EAP) has been extended until year end. 

Since we launched the EAP this summer, we’ve had so much interest from a wide range of freighters, shippers and other supply chain and logistics companies wanting to trial DeepPack’s innovative AI-powered Load Planning software. 

InstaDeep is proud to be a launch sponsor of Generation Logistics, a first of its kind UK national campaign to reframe the logistics industry for the future and recruit the next generation of talent.

A key focus for the campaign will be to demonstrate how the sector, which is critical to the way we live, is at the vanguard of innovation and technology – harnessing artificial intelligence, advanced robotics, autonomous vehicles and the decarbonisation of the supply chain. 

The logistics world provides for many extremely complex optimization problems. Bin packing and route planning are obvious examples. The number of possible packing configurations “explodes” exponentially with the number of items to be packed. It is impossible to find the optimal solution via the brute force method of enumerating all possible configurations and picking the best one (the number of possible configurations is orders of magnitude bigger than the number of atoms in the universe!).

Cargo Load optimization is the process of consolidating multiple shipments and/or arranging freight to use the capacity of trucks, railcars, pallets, and any other types of containers in the most efficient way.
Loading hundreds, or even thousands, of items in containers is an NP-hard problem, also called 3D Bin Packing Problem (3DBPP). It cannot be solved exactly due to the highly combinatorial nature of the problem.
To alleviate this, Freight-forwarders and shippers often use packing software that helps to optimize volume utilization and reduce shipment costs.