User Guide Overview

tilearn currently focuses on practical single-machine scheduling workflows. This section explains which algorithm to choose, how to prepare data, and how to map theory to executable Python code.

Core objective families

1) Minimize maximum lateness: $L_{\max}$

Use EDD (Earliest Due Date) ordering when due-date compliance is your primary concern.

• Primary guide: EDD
• API entry point: tilearn.edd

2) Minimize weighted completion behavior: $\sum w_j C_j$

Use WSPT ordering to prioritize higher-weight, shorter-processing jobs.

• Primary guide: WSPT
• API entry point: tilearn.wspt

3) Mixed-list orchestration with precedence flags

Use the multi-list pipeline when your workload combines:

• independent lists (prec=0), and

• precedence-style lists (prec=1).

• End-to-end guide: Illustrative workflow

• API entry point: tilearn.optimal_list

Recommended reading order

1. Start with Getting started.
2. Read either EDD or WSPT based on your objective.
3. Move to Illustrative workflow for multi-file production-like pipelines.
4. Use API reference for parameter-level details.

Pages in this section

📄️ Weighted Completion Time with WSPT

WSPT algorithm

📄️ Maximum Lateness with EDD

The Earliest Due Date (EDD) rule sorts jobs by increasing due date d_j.

📄️ End-to-End Illustrative Workflow

This guide shows a reproducible workflow for combining: