To work out bin bag size for a pile of items, you need three numbers: the common capacity of each bag, the size of every item in the same unit, and a deterministic way to assign each item to a bag without exceeding capacity. The Bin Packing Calculator does exactly that — you enter one bag capacity and up to 1,000 labelled sizes, then the tool sorts items largest first and places each one into the first open bag that has room, opening a new bag only when no existing bag can hold the item. It returns the number of bags opened, every assignment, used and remaining capacity per bag, overall utilization, and the simple size-based lower bound (the ceiling of total item size divided by capacity) so you can see whether the heuristic matches the theoretical minimum. The whole calculation runs in your browser with no upload, and the result is reproducible because equal sizes keep their original order and "first" always means the earliest open bag.
What "bin bag size" actually means in a packing problem
The phrase "bin bag size" can mean two different things: the capacity of one bag (how much it can hold), or the number of bags required for a fixed pile (how many to buy). Real-world waste streams confuse both, because people often buy bags by guess and end up with overflowing sacks or half-empty ones stacked by the curb. A scalar packing model collapses the question into a single dimension — volume, weight, length, or any one-dimensional resource — so every item and every bag share the same unit. That simplification lets a deterministic algorithm explore the assignment in a few milliseconds and report the smallest practical bag count for the items you actually have.
If your items are bulky but light, weight in kilograms is the wrong unit and the plan will mislead you. If they are dense and small, volume in litres is the wrong unit. Pick the constraint that actually binds in your situation — the one that fills first — and use it for both capacity and every item size. If you are unsure how to extract a usable size from a real box or package, the practical walkthrough on how to measure a packaging box for bin packing explains how to turn a physical object into a single scalar input.
Why a calculator beats guessing by hand
Bag-counting by eye fails for two reasons. First, human working memory holds only a handful of items at once, so larger piles force trade-offs nobody can see at the same time. Second, the bin-packing problem is computationally difficult — Google OR-Tools documents the minimum-bin objective and the constraint that no item can be split and no bag may exceed capacity, and the number of possible assignments grows very quickly with item count. A deterministic heuristic such as First Fit Decreasing (FFD) sidesteps the search cost: sort items largest to smallest, then drop each into the earliest open bag with room. It does not always find the smallest possible bag count, but it always finishes, always respects the constraints, and always returns the same answer for the same input.
A second quantity helps you judge the result: the lower bound, calculated as the ceiling of total item size divided by bag capacity. No solution can use fewer bags than that number, but matching it does not by itself prove the plan is optimal, because individual item combinations also constrain feasibility. If the heuristic returns more, the size-based floor is loose — exceeding it does not by itself mean a better arrangement exists. Lehigh University's analysis of cutting stock problems describes the same idea in a closely related setting, where a simple size ratio sets a floor that no arrangement can beat.
How to work out bin bag size with the Bin Packing Calculator
- Enter the common capacity available in every bag. This is the maximum size that one bag can hold in the unit you have chosen — litres, kilograms, metres, or any positive scalar.
- List one item per line as either a bare size or "label, size", using the same unit as the capacity. Rows that contain only a number receive automatic labels; rows with custom labels use exactly one comma to separate the label from the size.
- Submit the list — the tool accepts up to 1,000 items. Every item must be greater than zero and no larger than one bag's capacity, otherwise the tool returns a clear error rather than a misleading plan.
- Inspect every bag the plan opened: the items it received, the used capacity, and the remaining capacity.
- Read the overall utilization (total item size divided by total capacity of opened bags) and the size-based lower bound, then copy the plan for your worksheet, loading draft, or batch grouping.
Reading the bin packing plan
Three reported numbers tell you almost everything about the plan:
| Number | What it means | How to use it |
|---|---|---|
| Bins opened | How many bags the heuristic filled | Your order quantity |
| Utilization | Total item size divided by total opened-bag capacity | Higher means less empty space |
| Lower bound | Ceiling of total item size divided by bag capacity | A lower bound on the bag count |
If the bins opened equals the lower bound, the heuristic matched the size-based floor. If it is higher, the plan can still be globally optimal — useful when you want to compare orderings or test scenarios where locally sensible early placements block better later combinations. Each bag in the result also shows used and remaining capacity. Used capacity is the sum of assigned item sizes; remaining capacity is the scalar headroom, which may or may not correspond to recoverable physical volume in a real sack.
Limits of a scalar packing model
The calculator treats sizes as abstract scalar quantities. They can stand for weight, memory, workload, length, or any other one-dimensional resource as long as capacity and every item share the same unit. They do not represent three-dimensional box dimensions, orientation, balance, fragility, stacking strength, centre of gravity, hazardous separation, or vehicle axle limits. "Container loading" in the broad physical sense needs specialist software and safety rules, and the tool does not split items, combine capacities, reserve space, or apply values and priorities.
Worked check on the lower bound: with capacity 60 and item sizes 25, 25, 25, 20, 15, total item size is 25 + 25 + 25 + 20 + 15 = 110. Divide by 60 to get 1.8333..., and the ceiling rounds up to 2 — so no arrangement can fit those items in fewer than 2 bags, regardless of which algorithm you use.
Every input must be greater than zero and no larger than one bag's capacity. Negative or oversized items return a clear error rather than a misleading plan. Processing stays entirely in the browser, so your numbers never leave your device.
Practical scenarios where the plan helps
The plan is useful as a draft, not as a proof. Use it to:
- Estimate how many sacks you need for a garden clear-out, when every load is roughly the same kind of waste and weight is the binding constraint.
- Sketch a batch grouping for manufacturing runs where each run is one "bag" and each order is an "item".
- Compare ordering effects — change one item's size and see whether the heuristic opens more bags or packs more tightly.
- Teach the difference between a heuristic and an exact solver, using the lower bound as the reference floor.
For logistics, cloud capacity, manufacturing, or safety-critical loading, use a validated domain solver and confirm the final assignment independently. The plan returned here is transparent educational and planning assistance, not a certificate of minimum cost or safe loading.
For a deeper look, see Convert Bra Size From European to American.