Capacity Constraint

Capacity constraint research focuses on optimizing resource allocation in systems where limitations on resources (e.g., computing power, network bandwidth, physical space) restrict performance. Current research explores diverse approaches, including deep learning models like convolutional neural networks and graph neural networks, as well as optimization algorithms such as branch and bound and water-filling, to address these constraints in various applications. These efforts aim to improve efficiency and fairness in resource distribution, impacting fields ranging from smart manufacturing and telecommunications to healthcare and recommendation systems. The ultimate goal is to develop robust and adaptable strategies for managing capacity limitations and maximizing system performance under resource scarcity.