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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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Heterogeneous pigeon-inspired optimization

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Science China(Information Sciences) 2019年第7期62卷 64-72页

作者： Hao WANG Zhuxi ZHANG Zhen DAI Jun CHEN Xi ZHU Wenbo DU Xianbin CAO School of Electronic and Information Engineering Frontier Institute of Science and Technology InnovationBeihang University National Transportation Preparation Office School of Engineering and Materials Science Queen Mary University of London National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation

Pigeon-inspired optimization(PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator,and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally,which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of "exploitation" and "exploration", so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

关键词： heuristic optimization pigeon-inspired optimization particle heterogeneity network-based topology scale-free network selective-informed learning

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The influence of continuous tunnel driving on high-speed rail drivers’ brain responses

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Tunnelling and Underground Space technology 2025年 163卷

作者： Guo, Zizheng Pang, Huishan Li, Guofa Li, Zhenning Xu, Hui Green, Paul School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China Intelligent Comprehensive Transportation Key Laboratory of Sichuan Province Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China State Key Laboratory of Internet of Things for Smart City University of Macau Macau999078 China China Railway Chengdu Group Co. Ltd. Chengdu610031 China and the Department of Industrial and Operations Engineering University of Michigan Ann ArborMI48109 United States

The impact of tunnels on driver performance is significant, yet it remains understudied in the context of high-speed rail. This study investigated the effects of continuous tunnel driving on high-speed rail drivers’ mental fatigue and brain functional connectivity using electroencephalography (EEG) and graph theory. Forty-two participants performed simulated driving tasks in continuous tunnel and plain scenarios. Results demonstrated that tunnel scenarios significantly elevated subjective fatigue and prolonged reaction times (p © 2025 Elsevier Ltd

关键词： Network theory (graphs)

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Short-term origin-destination passenger flow forecasting in urban rail transit systems: An ensemble deep learning approach based on data augmentation

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Computers & Industrial engineering 2025年 206卷

作者： Xin Wang Dingjun Chen Xuze Ye Xia Zhao Shaoquan Ni School of Transportation and Logistics Southwest Jiaotong University Chengdu 611756 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China

Accurate network-level Origin-Destination (OD) passenger flow forecasting is crucial for enhancing the efficiency and service quality of urban rail transit (URT). In URT networks, a significant portion of the passenger flow comes from medium and low flow OD pairs. However, network-level OD passenger flow data exhibit characteristics such as high-dimensional sparsity, data availability, and strong randomness, which severely constrain the performance of forecasting models for medium and low flow OD pairs. In view of this, this study proposes an ensemble deep learning framework (PatchPF) with data augmentation at its core, aimed at short-term OD passenger flow forecasting at the URT network level. We introduce a novel BaggingT mechanism to implement time series ensemble forecasting in PatchPF to further improve the forecasting performance and robustness. The PatchPF architecture is tested on real-world metro datasets from Chongqing and Chengdu, China. The results indicate that it outperforms the other benchmark models. Moreover, the PatchPF architecture does not exhibit the performance bottlenecks in forecasting medium and low flow OD pairs that other state-of-the-art models do, demonstrating the effectiveness of PatchPF and its key components in OD passenger flow forecasting.

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Distribution Vehicle Routing Optimization Based on 3D Loading 4th

Distribution Vehicle Routing Optimization Based on 3D Loadin...

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4th International Conference on Smart Vehicular technology, transportation, Communication and applications, VTCA 2021

作者： Luo, Xuan Zhang, Jin Yin, Tingyu Zhu, Hongxing Qiu, Mingyue School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Comprehensive Transportation Big Data Application Technology Laboratory Chengdu611756 China

ISBN: (纸本)9789811640384

The rapid development of e-commerce leads to increasingly fierce competition in the logistics industry. Cost reduction and efficiency has become the key issue for logistics enterprises to survive. Loading optimization and path optimization are the two core issues in distribution activities, and they are inseparable and related. The joint optimization of the two is more in line with the actual distribution optimization needs. Based on the analysis of the interaction between routing and loading, this paper proposes a multi-objective optimization model with the minimum distribution cost, the minimum number of vehicles and the maximum customer satisfaction as the objectives. A hybrid nesting algorithm based on NSGAII algorithm is designed, in which the packing module is designed by tree search algorithm combined with the deepest and leftmost algorithm. Finally, the real data of China Railway Express Chengdu branch are taken as a case to verify the feasibility of the model and algorithm. Combined with the results of the algorithm, some suggestions are put forward. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Customer satisfaction

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An aggregate flow based scheduler in multi-task cooperated UAVs network

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Chinese Journal of Aeronautics 2020年第11期33卷 2989-2998页

作者： Xiaohuan LI Ziqi XIE Jin YE Xin TANG Chunhai LI Fengzhu TANG Rong YU School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China National Engineering Laboratory for Comprehensive Transportation Big Data Application Technology(Guangxi) Nanning 530001China School of Automation Guangdong University of TechnologyGuangzhou 510006China

Unmanned Aerial Vehicles(UAVs)cooperative multi-task system has become the research focus in recent ***,the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling,because they are not able to perceive the different *** this paper,a novel cooperated UAVs network framework for multi-task scheduling is *** is a three-layer network including a core layer,an aggregation layer and an execution layer,which enhances the efficiency of multi-task distribution,aggregation and ***,an Aggre Gate Flow(AGFlow)based scheduler is dedicatedly designed to maximize the task completion rate,whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each *** results demonstrate that,compared with that of state-of-the-art scheduler,the average task completion rate of AGFlow based scheduler is raised by 0.278.

关键词： Mixed-flow scheduling Multi-task Task completion rate Unmanned Aerial Vehicles(UAVs) Urgency-level

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Predictability of real temporal networks

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national Science Review 2020年第5期7卷 929-937页

作者： Disheng Tang Wenbo Du Louis Shekhtman Yijie Wang Shlomo Havlin Xianbin Cao Gang Yan School of Electronic and Information Engineering Beihang University Shanghai Institute of Intelligence Science and Technology Tongji University Network Science Institute Northeastern University Department of Physics Bar Ilan University CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation School of Physics Science and Engineering Tongji University

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and *** evidence has shown that the temporal nature of links in many real-world networks is not ***, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly,we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

关键词： temporal network predictability network entropy predictive algorithm

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Multimodal or intermodal: greenhouse gas emissions in less than container load in China–Pakistan trade

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Environment Systems and Decisions 2023年第2期43卷 265-280页

作者： Shoukat, Rizwan School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Lab of National United Engineering Laboratory of Integrated and Intelligent Transportation No. 111 North Second Ring Road Chengdu China

The most important factors to consider in transportation are the items being transported, the origin and destination locations, the length of the trip, the mode of transportation required (truck, train, plane, or ship), the shipping due dates, the costs, the level of security, regulation, and monitoring, and the costs. Specifically, the goal of this study is to determine the cost of, delivery time for, and greenhouse gas (GHG) emissions related to the import of less than container load (LCL) from China to Pakistan. For the multiobjective problem, we employed the mixed-integer linear programming (MILP) approach to construct it while keeping the supply and demand constraints in mind. We tackled the problem using a multiobjective evolutionary algorithm and developed Pareto optimal solutions that could trade-off cost, time, and GHG emission in order to achieve the best possible result. According to the data, which was received from one of Pakistan’s top logistics service companies, using intermodal transportation to carry a ten-tonne product from Shanghai in China to Kasur in Pakistan will cost 37 per cent more, take 14 per cent longer, and emit more greenhouse gases than using traditional multimodal transportation. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Genetic algorithm GHG emission Intermodal transportation costs Less than container Multiobjective problem

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Study on Valid Travel Route Selection of Integrated Passenger transportation 4th

Study on Valid Travel Route Selection of Integrated Passenge...

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4th International Conference on Smart Vehicular technology, transportation, Communication and applications, VTCA 2021

作者： Yang, Yao Kan, Chenyan Chen, Dingjun School of Transportation and Logistics Southwest Jiaotong University Chengdu610031 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest JiaoTong University Chengdu610031 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu610031 China

ISBN: (纸本)9789811640384

With the vigorous development of the integrated transportation network, the choice of passenger travel route is becoming more and more diversified. Therefore, it is important to build the integrated transportation network by establishing the cost function of valid travel route to measure the route that passengers may choose. Based on passenger travel demand, an optimization model of valid route selection for integrated transportation network was established based on generalized travel cost. According to the characteristics of the model, an improved BFS algorithm based on dynamic updating generalized cost function and determining valid travel route by key nodes was designed to solve the valid route. Finally, a simulation was given to verify the effectiveness of the model and algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost functions

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