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Deep reinforcement learning for solving vehicle routing problems with backhauls
The vehicle routing problem with backhauls (VRPBs) is a challenging problem commonly
studied in computer science and operations research. Featured by linehaul (or delivery) and …
studied in computer science and operations research. Featured by linehaul (or delivery) and …
Neural combinatorial optimization algorithms for solving vehicle routing problems: A comprehensive survey with perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
Machine learning to solve vehicle routing problems: A survey
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
PolyNet: Learning diverse solution strategies for neural combinatorial optimization
Reinforcement learning-based methods for constructing solutions to combinatorial
optimization problems are rapidly approaching the performance of human-designed …
optimization problems are rapidly approaching the performance of human-designed …
An edge-aware graph autoencoder trained on scale-imbalanced data for traveling salesman problems
In recent years, there has been a notable surge in research on machine learning techniques
for combinatorial optimization. It has been shown that learning-based methods outperform …
for combinatorial optimization. It has been shown that learning-based methods outperform …
Reinforcement learning-based nonautoregressive solver for traveling salesman problems
The traveling salesman problem (TSP) is a well-known combinatorial optimization problem
(COP) with broad real-world applications. Recently, neural networks (NNs) have gained …
(COP) with broad real-world applications. Recently, neural networks (NNs) have gained …
Instance-conditioned adaptation for large-scale generalization of neural combinatorial optimization
The neural combinatorial optimization (NCO) approach has shown great potential for solving
routing problems without the requirement of expert knowledge. However, existing …
routing problems without the requirement of expert knowledge. However, existing …
Risk control of epidemic in urban cold-chain transportation
S Liao, X Li, Y Niu, Z Xu, Y Cao - Sustainable Cities and Society, 2024 - Elsevier
The COVID-19 pandemic has severely disrupted the daily running of urban logistics system,
thereby increasing the resilience and health requirements in the design of sustainable …
thereby increasing the resilience and health requirements in the design of sustainable …
Collaborative deep reinforcement learning for solving multi-objective vehicle routing problems
Existing deep reinforcement learning (DRL) methods for multi-objective vehicle routing
problems (MOVRPs) typically decompose an MOVRP into subproblems with respective …
problems (MOVRPs) typically decompose an MOVRP into subproblems with respective …