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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

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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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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Integrated supply chain plan under multiple distribution networks: an implementation of mixed integer linear programming

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Circular Economy and Sustainability 2024年第4期4卷 2599-2623页

The study aims to minimize the cost of operations, storage, penalties, and dispatch in one of the largest paper and board manufacturing industries in Asia. The real-world data is obtained for cases underneath mixed products' in integrated and non-integrated supply chains. The cost minimization problem is formulated by developing the mixed integer linear programming (MILP) model constraining the multiple origins and destination ports in Pakistan. The metaheuristic optimization technique, such as the multi-objective genetic algorithm (MOGA), is applied based on epsilon dominance. The study results demonstrate that the cost of an integrated supply chain is minimal under various circumstances compared to the non-integrated supply chain, which provides a difference of 37% in both. Further, this study integrates the supply of paper and board products with the intermodal and multimodal transportation networks to investigate the most feasible supply chain network in the South Asian region. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Epsilon dominance Integrated and non-integrated Intermodal and multimodal South Asian region Supply chain risk

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Research on Equipment Management System of Railway Passenger Station Based on High-Precision Positioning 5th

Research on Equipment Management System of Railway Passenger...

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5th International Conference on Smart Vehicular technology, transportation, Communication and applications, VTCA 2022

作者： Li, Lexi Wang, Bozhou Liu, Zhen Ni, Shaoquan School of Transportation and Logistics Southwest Jiaotong University Sichuan 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)9789819908479

The equipment management informatization and intelligentization in railway passenger stations is a significant component of the construction of intelligent stations. The current equipment management of railway passenger stations is slowly developing and backward in its approach. This paper studied the weaknesses of equipment management in railway passenger stations, conducted demand analysis, and proposed the development direction of equipment management in railway passenger stations with the support of theoretical basis and technical basis. The technical architecture of the high-precision positioning-based railway passenger station equipment management system was built from the terminal layer, application layer, service layer, data layer and perception layer, and four functional modules were designed for equipment management: equipment real-time dynamic trackment, full life-cycle management, equipment fault responsibility traceability, and incident response. The key technologies required for the construction of the system were studied, including Bayesian fusion-based equipment positioning technology with high accuracy and neural network-based remaining life prediction technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Informatization

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