Combinatorial Optimization Problem

Combinatorial optimization problems (COPs) involve finding the best solution from a vast, discrete set of possibilities, aiming to maximize or minimize an objective function subject to constraints. Current research heavily focuses on applying machine learning, particularly deep learning models like graph neural networks and reinforcement learning, alongside traditional methods such as constraint programming, dynamic programming, and local search heuristics, to improve solution quality and efficiency. These advancements are impacting diverse fields, including healthcare scheduling, logistics, and network optimization, by enabling faster and more effective solutions to complex real-world problems. The development of unified frameworks and standardized benchmarks is also a key area of focus to facilitate broader comparison and progress within the field.