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 …

Recent neural methods for vehicle routing problems always train and test the deep models
on the same instance distribution (ie, uniform). To tackle the consequent cross-distribution …

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 …

In marine container terminals, truck dispatching optimization is often considered as the
primary focus as it provides crucial synergy between the sea-side operations and yard-side …

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …

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

As a research hotspot across logistics, operations research, and artificial intelligence, route
planning has become a key technology for intelligent transportation systems. Recently, data …

This work presents a graph neural network (GNN) framework for solving the maximum
independent set (MIS) problem, inspired by dynamic programming (DP). Specifically, given …

With the rapid growth of the on-demand logistics industry, large-scale pickup and delivery
with soft time windows has become widespread in various time-critical scenarios. This …

Effective scheduling of multiple agricultural machines in emergencies can reduce crop
losses to a great extent. In this paper, cooperative scheduling based on deep reinforcement …