The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …

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 …

We present NeuroLKH, a novel algorithm that combines deep learning with the strong
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …

Existing deep reinforcement learning (DRL)-based methods for solving the capacitated
vehicle routing problem (CVRP) intrinsically cope with a homogeneous vehicle fleet, in …

Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …

Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN)
with split loads and time windows involves a customer demand splitting strategy and a multi …

Deep reinforcement learning (DRL) has recently shown its success in tackling complex
combinatorial optimization problems. When these problems are extended to multiobjective …

This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at
efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the …

We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …