A curated dataset of multi-channel TV program schedules for optimization and benchmarking

TL;DR

This paper introduces a curated dataset of TV schedules for benchmarking and optimizing scheduling algorithms.

Contribution

The paper provides a standardized dataset of 515 TV scheduling instances from multiple sources for algorithm benchmarking.

Findings

The dataset includes program schedules converted into a consistent format with time consistency.

Each instance includes constraints and quality scores for algorithm testing.

The dataset supports benchmarking optimization methods and constraint-based scheduling.

Abstract

This article presents a collection of 515 Smart TV scheduling instances from three public sources: IPTV EPG broadcast listings, EPG.PW program guides, and upcoming YouTube livestream schedules obtained through the YouTube Data API. The data were collected and processed using automated Python scripts that download program information, convert it into a standard format, and remove entries with missing or invalid time information. All times are recorded as minutes from the start of each scheduling period to ensure consistency across different sources.

Each instance is stored as a JSON file with a consistent structure. Each file contains the scheduling period (start and end times), channel information, program time slots that don't overlap, program categories, and quality scores from 0 to 100 for each program. Instances also include two types of constraints: strict rules that specify which channels must be available during certain time windows, and flexible preferences that assign bonus points to programs airing at preferred times. All files are organized by data source and automatically checked for correctness.

The dataset can be used for benchmarking optimization and constraint-based scheduling methods, comparing objective formulations with switching and termination penalties, testing algorithms under time-dependent feasibility constraints, and extracting instance features for dataset characterization and dimensionality-reduction workflows.

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