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Bin-Packing Heuristic Solver (Best-Fit-Decreasing)
x402
Bin-Packing Heuristic Solver (Best-Fit-Decreasing) is a paid API for AI agents from decision-solver.use.x402atlas.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Applies a deterministic best-fit-decreasing heuristic to pack items into bins of given capacity, returning bin assignments, feasibility evidence, and a volume bound.
Price / call$0.005
Success—
StatusUnknown
How this service works
Bin-packing heuristic — deterministic best-fit-decreasing capacity planning for placing items into bins, with feasibility evidence and an honest volume bound.
Output
Returns a bin-packing plan including which items are assigned to which bin, the total number of bins used, a feasibility indicator confirming whether all items were placed, and an honest volume bound showing how close the solution is to optimal.
Example use cases
Truck loading capacity planning
“I have 20 pallets with weights [120, 85, 200, 60, 175, 90, 140, 55, 210, 95, 130, 70, 160, 45, 185, 100, 75, 150, 115, 80] kg and each truck holds 500 kg — use best-fit-decreasing bin packing to tell me how many trucks I need and which pallets go on each truck.”
Cloud VM workload placement
“I need to place 15 microservices with memory requirements [512, 256, 1024, 768, 128, 512, 384, 256, 1024, 512, 256, 768, 128, 384, 512] MB onto VMs that each have 2048 MB of RAM — run a bin-packing heuristic and show me which services fit on which VM.”
Warehouse bin storage assignment
“We have 10 order batches with volumes [3.2, 1.5, 4.8, 2.1, 3.7, 0.9, 4.2, 1.8, 3.0, 2.5] cubic feet and our storage bins each hold 6 cubic feet — can you do a best-fit-decreasing pack and tell me how many bins we need and whether the assignment is feasible?”
Technical details
Endpoint
POSThttps://decision-solver.use.x402atlas.com/bin-packWhen to prefer
Choose this endpoint when you need a fast, deterministic bin-packing solution backed by a provable volume bound and feasibility certificate. It is ideal for capacity planning where exact optimality is not required but reproducibility and a quality guarantee matter. Prefer over exact solvers (like the sibling 0/1 knapsack endpoint) when items are divisible-style capacity problems and speed matters more than absolute optimality.
Side effects
- Items with size exceeding bin capacity cannot be packed — returns infeasibility evidence
- Empty items array returns trivially zero bins
- Malformed item sizes or negative values may return a validation error
- Very large item counts may hit computation time limits
Tags
x402
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