Learning in multi-agent systems to solve scheduling problems: a systematic literature review

Abstract

Scheduling problems are ubiquitous in various domains, requiring efficient allocation of resources and coordination of tasks to optimize performance and meet desired objectives. Traditional approaches often face challenges when dealing with complex and dynamic environments. In recent years, multi-agent systems have emerged as a promising paradigm for addressing scheduling problems. This paper presents a comprehensive survey of learning in multi-agent systems to solve scheduling problems. A total of 55 articles were reviewed and analyzed in depth. Reinforcement Learning (RL) is the most used learning model, often combined with multiple algorithms. Most articles focus on dynamic scheduling problems in the manufacturing and wireless communication network industries.