In this article we cover:
- What is 3D bin packing?
- What does 3D bin packing software do?
- Why is optimal packing counter-intuitive?
- Types of 3D bin packing configurations?
- What are the main use cases of 3D bin packing?
What is 3D Bin Packing?
3D bin packing is a mathematical optimization problem of placing items into fixed-size containers to maximize space utilization.
In logistics, companies use it to cut packaging and transport costs by fitting items into boxes, building denser pallets, and planning fuller loads for trucks, containers, and aircrafts.
In supply chain context, it’s often mentioned as cartonization (choosing the right box and maximizing its fill rate) palletization (building a full and stable pallet) or truck load planning (filling a compliant trailer while respecting the unloading sequence). Finding the optimal arrangement is hard, especially at scale, practical solutions rely on specialized algorithms to reach high-quality results.
How does 3D Bin Packing software work?
3D bin packing software takes dimensions, quantities and handling rules (such as orientation of object, like "this side up" information) as input, as well as the size constraints of the containers - such as maximum weight, maximum volume.
The software then generates a packing plan that arranges the objects inside the container (box, pallet, envelope), to maximize the utilization of the available space. The software may use different algorithms to generate the packing plan, such as heuristic approaches (including quick volume-only estimators like liquid cubing), 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 (ERP) systems to assist teams in optimizing the packing process.
To use 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.
Why optimal 3D Bin Packing can be counter-intuitive
In logistics operations, “optimal” is usually defined by practical objectives like keeping the transport costs low by minimizing the number of used boxes, maximizing fill rate, keeping weight and stackability within limits, and honoring handling rules like “this side up.”
Why is it counterintuitive? Because the lowest total shipping cost isn’t always achieved by using the fewest boxes. Two smaller cartons can be cheaper than one tightly packed box.
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.
These kind of loopholes frequently appear in carrier rates.
Carriers no longer price parcels on a simple flat fee. Most use zone-based tables, dimensional weights and add surcharges to steer parcels toward what fits their networks. Common factors include:
- dimensional/volumetric weight (chargeable weight = the larger of DIM or actual),
- oversize and overweight thresholds,
- zone/distance-based base rates,
- residential, delivery-area, weekend, and fuel surcharges.
A single, well-filled box can cross a DIM or oversize limit and become more expensive to ship. Splitting the order into two boxes may drop each parcel below a threshold and reduce the total bill.
Because each carrier’s rates and surcharges differ and change over time, fulfillment and distribution centers cannot rely on packers to consider the carrier rates during packing operations. Human judgment and basic “fill-rate only” cubing miss these pricing steps.
In order to stay on top of the carrier rates, logistics providers typically go for cost aware 3D cartonization and 3D palletization solutions.
3D Bin Packing use cases
In logistics, 3D bin packing problem commonly appears in packing orders into boxes (cartonization) and building loads on pallets (palletization). Both must account for real handling constraints such as item orientation (“this side up”), stackability and crush limits and carrier rates.
It’s a common challenge for logistics providers, and the impact varies by product mix and process design. Below are the main use cases where 3D bin packing supports box packing and pallet building.
Guidance during packing
Generates step-by-step instructions at the pack station or during pallet building. Show 2D or 3D visualizations, pack sequence, allowed orientations, apply carrier rules (including DIM). Allow operators to reduce handling time, material use, and surcharge exposure.
During pick-to-box
Assign the right box type at order release, so items go straight into the destination box during picking. This works best when SKU dimensions/weights and a maintained box library are in place. Pickers start with the final carton (or a tote mapped to it), reducing touches and queues at pack. Monitor repack rate, touches per order, and label reprints.
Calculate sales orders
Calculate the right shipping quotes at order entry (web-shops), considering optimal packing configuration of each order. Expose predicted cartons/pallets, weights, and DIM-rated sizes during checkout or order capture so quotes match reality and carrier selection improves. Use fallbacks for missing dimensions, keep units consistent (cm/in, kg/lb), and compare predicted vs. actual parcels as a control.
Create the perfect box every time
Make on-demand packing systems smarter, by calculating perfect-fit cartons. For box-on-demand or auto-cartoners, compute internal target dimensions (including allowances for padding or liners) and let the machine cut/score to spec. Set min/max limits for speed and safety, and review historical orders quarterly to refine standard sizes. Measure corrugate per order, average box utilization, and pack cycle time.
Summary
3D bin packing is a class of optimization problems used to choose right-sized boxes, build stable pallets, and plan fuller vehicle loads under real-world constraints. It’s computationally hard in the general case (NP-complete), so practical systems rely on fast heuristics and metaheuristics, fed by accurate product and container data, and tuned to business objectives like cost, fill rate, and handling rules.
Questions?
In logistics, 3D bin packing is the problem of arranging items into containers so they fit and ship efficiently, either into boxes (cartonization) or onto pallets (palletization). It targets minimal space or cost while respecting handling limits like orientation, stackability, weight, and carrier rules.
Common use cases include packing guidance at packing station or during pallet building (step-by-step instructions), pick-to-box (assigning the final carton at release to avoid repacking), order-entry quoting (predicting cartons/pallets and DIM weight for accurate shipping prices), box-on-demand (calculating right-size cartons to cut corrugate and shipped air), and vehicle loading (arranging pallets to meet axle limits and unload sequence for multi-stop routes).
There are multiple options, but Optioryx is often the best fit when you need one layer to coordinate warehouse flow end to end: it provides 3D cost cartonization that considers handling rules and carrier pricing, 3D palletization with advanced pallet-building algorithms and clear instructions during pick to pallet, and picking optimization linked to pallet building so the shortest route still produces stable pallets; in practice, that combination helps teams align box choice, pallet stability, and picker paths without replacing the WMS.
The optimal packing of items can sometimes be counterintuitive because of carrier pricing rates, especially dimensional weight, oversize thresholds, and surcharges, can make solutions that look “less packed” cost less. For example, splitting an order into two smaller cartons may avoid DIM charges or an oversize fee and end up cheaper than one large, high-fill box.
Yes. Trials are available on request so you can evaluate cartonization, palletization, and related workflows with your own data.