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End-to-End Illustrative Workflow

This guide shows a reproducible workflow for combining:

  • independent job lists (prec=0), and
  • precedence-style job lists (prec=1)

with tilearn.optimal_list.

Project structure​

project/
├── run.py
└── data/
    ├── backup/
    ├── list1.csv
    ├── list2.csv
    └── list3.csv

The backup/ directory is used as temporary working storage.

CSV format​

All lists use:

Name,p,r,d,w

Example:

Name,p,r,d,w
J1,4,0,100,0.65
J2,1,0,100,0.84
J3,3,0,100,0.46

Runnable script​

import csv
import tilearn as tl
from tilearn import plat as pl

original = "data"
backup = "data/backup"

lists = [
    pl.List(backup_path=backup, file_path="data/list1.csv", prec=0),
    pl.List(backup_path=backup, file_path="data/list2.csv", prec=1),
    pl.List(backup_path=backup, file_path="data/list3.csv", prec=1),
]

schedule = tl.optimal_list(lists, original, backup)

with open("output.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f)
    writer.writerow(["Name", "p", "r", "d", "w", "factor"])
    writer.writerows(schedule)

print("Generated output.csv with", len(schedule), "rows")

Run:

pip install tilearn
python run.py

Behavior notes​

  • prec=0: list is ranked with non-precedence factor (w/p behavior).
  • prec=1: list is ranked with cumulative precedence-style factor (sum(w)/sum(p) along list order).
  • optimal_list updates files inside backup/ during processing and clears temporary CSV files at the end.

Use a dedicated backup folder for safe runs.

Continue learning​

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