This paper aims to explore the optimization of heuristic scheduling algorithms for mobile robotics. The primary focus is on how these algorithms improve effectiveness in industrial and logistical environments, addressing a significant knowledge gap within the field.
The study is a systematic literature review, examining various optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), among others, as well as the improved versions of these fundamental algorithms. These algorithms are evaluated for their adaptability and efficiency in industrial and logistical environments characteristic of mobile robotics.
The analysis indicates that heuristic algorithms significantly improve scheduling optimization in areas related to robotic operations, particularly in optimizing time, distance, energy and cost. This systematic review assesses and ranks these algorithms, clarifying their effectiveness in diverse industrial and logistical contexts where such optimizations are crucial.
The paper identifies gaps and potential improvements in current approaches, highlighting the need for more flexible, real-time decision-making algorithms suited for mobile robotics. Furthermore, the paper proposes the exploration of hybrid heuristic algorithms as a means to achieve more effective scheduling optimization. This comprehensive analysis serves as a guide for future research on enhancing the operational efficiency of mobile robots in complex scheduling environments.
