Getting started

tilearn is a Python library for single-machine scheduling workflows, including:

• weighted completion-time ranking (WSPT),
• due-date-first ranking (EDD),
• mixed multi-list orchestration with optional precedence mode.

Install

pip install tilearn

Verify installation:

python -c "import tilearn; print('tilearn imported successfully')"

First run

import tilearn as tl

jobs = [
    ["J1", 2.0, 0, 8, 4.0],
    ["J2", 1.0, 0, 5, 1.0],
    ["J3", 3.0, 0, 6, 6.0],
]

print("WSPT:")
for row in tl.wspt([r[:] for r in jobs]):
    print(row)

print("\nEDD:")
for row in tl.edd([r[:] for r in jobs]):
    print(row)

Data format

Most APIs expect rows in this order:

[name, p, r, d, w]

• name: job identifier (str)
• p: processing time
• r: release time
• d: due date
• w: weight

Many helper functions append computed columns (for example w/p, completion time, lateness) directly to each row.

Problem notation

TiLearn follows standard scheduling notation:

$$\alpha \mid \beta \mid \gamma$$

• \alpha: machine environment (1 for single machine),
• \beta: constraints (for example prec, r_j),
• \gamma: objective (for example L_{\max}, \sum w_j C_j).

Where to go next

• User Guide: theory + practical workflows.
• API reference: function-level contracts.
• Building from source: contributor setup.