An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …

The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …

Abstract Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been
successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

Abstract Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large
number of combinatorial optimization problems. Recently, it has been shown that Large …

Abstract Machine learning techniques have attracted increasing attention in learning Branch-
and-Bound (B&B) variable selection policies, but most of the existing methods lack …

Objective-space decomposition algorithms (ODAs) are widely studied for solving multi-
objective integer programs. However, they often encounter difficulties in handling scalarized …

Abstract The Adaptive Large Neighborhood Search (ALNS) algorithm has shown
considerable success in solving combinatorial optimization problems (COPs). Nonetheless …

Primal heuristics are important for solving mixed integer linear programs, because they find
feasible solutions that facilitate branch and bound search. A prominent group of primal …