Curious about how companies can optimize their packaging processes and reduce transportation costs by efficiently fitting items into boxes and onto pallets?
The solution is 3D bin packing, an optimization technique used to maximize space utilization while minimizing empty or unutilized space.
Although often associated with cartonization, 3D bin packing has a range of use cases beyond packing items in boxes. It can also be used to optimize the arrangement of items on pallets or to determine the most efficient placement of products and pallets within trucks, containers, and airplanes.
In this blog, we'll explore the fundamentals of 3D bin packing and dive into its various applications.
What is 3D Bin Packing?
3D bin packing is a mathematical optimization problem of packing objects of different sizes and shapes into a limited number of three-dimensional containers (bins) to maximize space utilization and minimize empty space.
This problem arises in various settings, such as manufacturing, packaging, and transportation, where efficient use of space is crucial to reduce costs and increase the profitability of transported goods.
The goal of 3D bin packing is to determine the optimal placement of objects in containers, which can be a challenging task, particularly when dealing with a large number of orders.
Therefore, various algorithms and approaches have been developed to solve 3D bin packing efficiently and effectively.
How does 3D bin packing software work?
The 3D bin packing software takes input as the dimensions and quantities of the objects to be packed, as well as the dimensions and constraints of the containers - such as maximum weight, maximum volume, and a maximum number of containers.
The software then generates a packing plan that arranges the objects inside the containers, to maximize the utilization of the available space. The software may use different algorithms to generate the packing plan, such as heuristic approaches, exact algorithms, or metaheuristics.
Heuristic approaches rely on rules of thumb or intuition to generate a feasible packing plan, without guaranteeing that it is optimal.
Exact algorithms, on the other hand, guarantee that the generated packing plan is optimal, but they may be computationally intensive and impractical for large instances of the problem.
Metaheuristic algorithm compromises between heuristic approaches and exact algorithms, as they search for solutions by iteratively improving upon an initial solution. They are often faster than exact algorithms and can handle larger instances of the problem.
Once the software generates a packing plan, it can output the optimal arrangement of the objects in the containers, as well as provide visualizations of the packing plan to aid in manual packing.
3D bin packing software can be integrated within Warehouse Management Systems (WMS), Transport Management Systems (TMS), and Enterprise Resource Planning (EPR) systems to automate the packing process and streamline logistics operations.
To utilize the software all you have to do is upload the dimensional information of your items, define load constraints for specific or all items, choose in what container or vehicle you want the items to be loaded, and view the 3D visualizations & packing instructions.
Types of 3D Bin Packing configurations
There are different types of 3D bin packing configurations, depending on the number of bins involved and the objective of the packing process. Here are three common configurations:
For single-bin 3D packing, the objective is to pack a set of items into a single container or bin while minimizing unused space. The goal is to achieve the highest possible fill rate, which is the percentage of the volume of the container that is occupied by the items.
Single-bin 3D packing is used when products of different sizes and shapes need to be shipped in a cost-efficient manner.
For multiple-bin 3D packing, the objective is to pack a set of items into multiple containers or bins with objectives such as:
- Maximizing the fill-rate of bins
- Minimizing the number of bins used while maximizing the fill-rate.
- Optimizing transport costs by taking into account different rate structures as well as possible surcharges which vary depending on the carrier.
In the scenario above, the carrier imposes a rate structure based on the weight of the boxes. However, by splitting an order into two separate packages, the rate structure is exploited, leading to a 42% decrease in transport costs.
There are also custom 3D bin packing configurations that can include additional variables beyond the basic configurations mentioned earlier. These variables can add more complexity to the packing problem, such as
- Weight constraints, where a set of items need to be packed into multiple containers or bins while taking into account the weight restrictions of vehicles. This can include constraints on axle weight loads or total weight limits for the entire vehicle.
- Loading / unloading sequence. On top of making sure the axle weight and total weight limits are respected, you have to take into account the loading sequence as well, such that the shipper can unload the goods without having to take unnecessary ones out to make deliveries on time.
How can 3D Bin Packing help you?
Through optimized 3D bin packing, businesses can improve the process of cartonization, pallet load building, and truck load building.
The cartonization process can be improved by helping packers choose the correct size and number of boxes for each order to maximize the fill rate, avoid restacking, and reduce handling time.
Optimize your pallet load building by calculating the number and size of pallets you need for your order and consider factors such as the stackability of boxes onto a pallet, weight restrictions, and stability of the loaded pallet.
Optimize and automate your truck load building by packing items onto trucks or other vehicles, taking into account the available space, weight restrictions, and loading constraints specific to the vehicle being used.
Interested to know how we do it? Grab your API key for Optioryx’s bin-packing algorithm here.
