… new learning-based approach to generating high… procedure begins again. This algorithm
is referred to as branch-and-bound. Linear programming is a main workhorse of this procedure, …

… large neighborhood search (LNS) framework for vehicle routing that integrates learned
heuristics for generating new solutions. The learning … sophisticated high level search procedure. …

… simple nonlinear activation function that is fixed before training, and \(w^{i,j}\… training
procedure. In its simplest and most common form, the activation function would be the rectified linear …

… the policy is restricted to take the form of a decision tree. We propose two different approaches:
one using a mixed integer program formulation and the other using a tree-search based …

… al. 2005), where potentially a large fraction of the variables are fixed before then calling the
MIP solver recursively as part of a procedure for finding improved integer feasible solutions. …

… to learn to schedule a job-shop problem (JSSP) using a graph neural network (GNN) and
reinforcement learning (… framework employs a GNN to learn that node features that embed the …

… on a combination of mixed-integer linear programming (MILP) and machine learning (ML),
to … the first iteration of the procedure may significantly slow down the MIP optimization time. …

… automatically learn high-quality solution picking policies that … , ie, the solution picked by the
policy will always be accepted, to … , our method can learn high-quality policies that outperform …

… by considering mixed-integer linear programs (MILP) … policy of an agent is and what is the
optimal policy. Here we will deep-dive into the RL algorithms that search for the optimal policy …

… The time window in this study is considered to be hard. This means that the delivery can …
We begin with providing the overall procedure of the ALNS used in this paper. Then, we discuss …