Adaptive Problem-solving for Large-scale Scheduling Problems: A Case Study

TL;DR

This paper presents an adaptive problem-solving approach that automatically learns domain-specific heuristics to efficiently solve large-scale satellite scheduling problems, reducing computation time and increasing solvability.

Contribution

It introduces a learning-based method for automatic heuristic acquisition tailored to satellite scheduling, improving efficiency and success rates over traditional techniques.

Findings

Reduced CPU time for schedule generation

Increased percentage of solvable problems within resource limits

Effective application to real-world satellite mission data

Abstract

Although most scheduling problems are NP-hard, domain specific techniques perform well in practice but are quite expensive to construct. In adaptive problem-solving solving, domain specific knowledge is acquired automatically for a general problem solver with a flexible control architecture. In this approach, a learning system explores a space of possible heuristic methods for one well-suited to the eccentricities of the given domain and problem distribution. In this article, we discuss an application of the approach to scheduling satellite communications. Using problem distributions based on actual mission requirements, our approach identifies strategies that not only decrease the amount of CPU time required to produce schedules, but also increase the percentage of problems that are solvable within computational resource limitations.