Examples
Examples
Try the starting example
- JSON Pieces [ID,length mm,integer quantity]
- [["A",400,3],["B",250,2]]
- Stock length (mm)
- 1000
- Reserved cutting loss per piece (mm)
- 0
- Cut plan
- [{"cuts":[["A",400],["A",400]],"remaining":200},{"cuts":[["A",400],["B",250],["B",250]],"remaining":100}]
- Stock units
- 2
- Total remaining stock length
- 300
- Oversize pieces
- []
Try another scenario
- JSON Pieces [ID,length mm,integer quantity]
- [["A",450,3],["B",250,3]]
- Stock length (mm)
- 1000
- Reserved cutting loss per piece (mm)
- 0
- Cut plan
- [{"cuts":[["A",450],["A",450]],"remaining":100},{"cuts":[["A",450],["B",250],["B",250]],"remaining":50},{"cuts":[["B",250]],"remaining":750}]
- Stock units
- 3
- Total remaining stock length
- 900
- Oversize pieces
- []
Calculation method
Calculation method
Cut plan
Expand quantities; sort length descending then ID; first-fit into stock bars including one loss per cut.
Stock units
Number of bars opened by first-fit decreasing.
Total remaining stock length
Sum stock-length minus allocated piece lengths and reserved cut losses.
Oversize pieces
Pieces whose length plus one cutting loss exceeds stock.
Symbols and units
- a
- JSON Pieces [ID,length mm,integer quantity]
- b
- Stock length (mm)
- c
- Reserved cutting loss per piece (mm)
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- Folder-to-box index: folder thicknesses are indivisible lengths, boxes are identical usable thickness capacity, cutting loss zero. Returned per-box folder IDs form the retrieval index; ordering is by thickness heuristic, not archival classification. Supported limits: At most 100 expanded pieces. Straight cuts only; no bending or structural design. Heuristic is feasible but may not minimize stock count. Loss is reserved even on the last cut.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.
Further reading
Background reading; this does not imply endorsement or independent review of SumFizz.