Mixed Integer Linear

Mixed-integer linear programming (MILP) tackles optimization problems where some variables must be integers, a common constraint in real-world scenarios. Current research focuses on improving the efficiency of MILP solvers, exploring techniques like advanced branching strategies (e.g., discounted pseudocosts), cutting-plane generation and selection (often enhanced by machine learning models such as graph neural networks and reinforcement learning), and novel heuristic approaches to handle large-scale problems. These advancements are crucial for addressing diverse applications, from supply chain optimization and resource allocation to network interdiction and the design of robust systems, significantly impacting various fields by enabling the solution of previously intractable problems.