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Full-Text Articles in Physical Sciences and Mathematics
Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang
Research Collection School Of Computing and Information Systems
Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to …
Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen
Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen
Research Collection School Of Computing and Information Systems
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated …
Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee
Learning Generalizable Models For Vehicle Routing Problems Via Knowledge Distillation, Jieyi Bi, Yining Ma, Jiahai Wang, Zhiguang Cao, Jinbiao Chen, Yuan Sun, Yeow Meng Chee
Research Collection School Of Computing and Information Systems
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results …
Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Vehicle Routing: Review Of Benchmark Datasets, Aldy Gunawan, Graham Kendall, Barry Mccollum, Hsin-Vonn Seow, Lai Soon Lee
Research Collection School Of Computing and Information Systems
The Vehicle Routing Problem (VRP) was formally presented to the scientific literature in 1959 by Dantzig and Ramser (DOI:10.1287/mnsc.6.1.80). Sixty years on, the problem is still heavily researched, with hundreds of papers having been published addressing this problem and the many variants that now exist. Many datasets have been proposed to enable researchers to compare their algorithms using the same problem instances where either the best known solution is known or, in some cases, the optimal solution is known. In this survey paper, we provide a list of Vehicle Routing Problem datasets, categorized to enable researchers to have easy access …
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou
Research Collection School Of Computing and Information Systems
This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Using Reinforcement Learning To Minimize The Probability Of Delay Occurrence In Transportation, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Zhengghua Chen, Le Zhang, Xuexi Zhang
Research Collection School Of Computing and Information Systems
Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual …
Real-Life Vehicle Routing With Non-Standard Constraints, Wee Leong Lee
Real-Life Vehicle Routing With Non-Standard Constraints, Wee Leong Lee
Research Collection School Of Computing and Information Systems
Real-life vehicle routing problems comprise of a number of complexities that are not considered by the classical models found in vehicle routing literature. I present, in this paper, a two-stage sweep-based heuristic to find good solutions to a real-life Vehicle Routing Problem (VRP). The problem I shall consider, will deal with some non-standard constraints beyond those normally associated with the classical VRP. Other than considering the capacity constraints for vehicles and the time windows for deliveries, I shall introduce four additional non-standard constraints: merging of customer orders, controlling the maximum number of drop points, matching orders to vehicle types, and …