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A VNS-NPGA Approach to Multi-Objective Optimization of Hub-and-spoke Logistics Network

Journal of Traffic and Transportation Engineering(English Edition) 2024年

作者： Changxi Ma Chuwei Shi Yun Yang Yongpeng Zhao Zhuye Xu Bo Du School of Traffic and Transportation， Lanzhou Jiaotong University Key Laboratory of Railway lndustry on Plateau Railway Transportation Intelligent Management and Control， Lanzhou Jiaotong University School of Tourism and Management， Yangling Vocational and Technical College School of New Energy and Power Engineering， Lanzhou Jiaotong University National Engineering research Center of Highway Maintenance Technology， Changsha University of Science & Technology SMART Infrastructure Facility， University of Wollongong

To realize low-cost freight transport in the logistics network and improve the network operation efficiency， a multi-objective optimization model and the corresponding algorithm for a hub-and-spoke logistics network are proposed based on the multi-level location of hub points and channels layout. By considering the structure of the multi-level hub-and-spoke logistics network and the features of the connectivity between the hub and spoke points， the multi-objective optimization model is constructed with two objectives of minimizing the total network operation costs and the total network service time. By considering the characteristics of decision variables and models， a Variable Neighborhood Search (VNS)-Niched Pareto Genetic Algorithm (NPGA) approach with a three-stage encoding structure chromosome is proposed， where the VNS algorithm nested in NPGA is used for individual variable neighborhood search to optimize individual channel level genes， and NPGA is adopted to solve the multi-objective optimization model. To evaluate the performance of the proposed VNS-NPGA approach， a real-life case study based on a small-scale Australia Post data set was conducted， and 25 nodes of the Australia Post and 14 nodes of the gansu province 3-level hub-and-spoke logistics networks were established， respectively. The analysis results indicated that the network structure of multi-level hub points could avoid the detour problem existing in the traditional hub-and-spoke network， and showed better applicability in the narrow geographical structure. Compared to the traditional multi-objective evolutionary algorithms， VNS-NPGA can obtain better solutions through the distributed optimization of channel levels， avoiding the problem that a single algorithm cannot effectively deal with coupling relationships in genes.

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Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

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Journal of Traffic and Transportation Engineering(English Edition) 2022年第6期9卷 893-911页

作者： Jinxing Shen Kun Liu Changxi Ma Yongpeng Zhao Chuwei Shi College of Civil and Transportation Engineering Hohai UniversityNanjing 210098China School of Traffc and Transportation Lanzhou Jiaotong UniversityLanzhou 730070China

Determining the optimal vehicle routing of emergency material distribution(VREMD)is one of the core issues of emergency management,which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency *** summarize the latest research progress,we collected 511VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer ***,we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords,research gaps,and future *** results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published ***,these organizations with the most publications performed poorly regarding the number of literature *** and the US have contributed the vast majority of the literature,and there are close collaborations between researchers from both *** optimization model of VREMD can be divided into single-,multi-,and joint-objective *** shortest travel time is the most common optimization objective in the single-objective optimization *** scholars focus on multiobjective optimization models to consider conflicting objectives *** recent literature,scholars have focused on the impact of uncertainty and special events(e.g.,COVID-19)on ***,some scholars focus on joint optimization models to optimize vehicle routes and central locations(or material allocation)*** algorithms can be divided into two primary categories,i.e.,mathematical planning methods and intelligent evolutionary *** branch and bound algorithm is the most dominant mathematical planning algorithm,while genetic algorithms and their enhancements are the most co

关键词： Transportation engineering Vehicle routing of emergency materials distribution Bibliometric analysis Optimization model Solution algorithms

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A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19: A case study of Hubei province

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Journal of Traffic and Transportation Engineering(English Edition) 2022年第1期9卷 1-20页

作者： Yun Yang Changxi Ma Jibiao Zhou Sheng Dong Gang Ling Jiangchen Li School of Traffic and Transportation Lanzhou Jiaotong UniversityLanzhou 730070China College of Transportation Engineering Tongji UniversityShanghai 201804China School of Civil and Transportation Engineering Ningbo University of TechnologyNingbo 315211China department of Civil&Environmental Engineering University of AlbertaEdmontonAB T6G 1H9Canada

For the optimization problem of the cold-chain emergency materials(CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization(MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis(IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei province. research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.

关键词： Cold-chain emergency logistics Multidimensional objective robust optimization Distribution path Hybrid algorithm Fuzzy theory

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Prediction of Remaining Parking Spaces Based on EMD-LSTM-BiLSTM Neural Network

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Journal of Traffic and Transportation Engineering(English Edition) 2023年

作者： Changxi Ma Xiaoting Huang Ke Wang Yongpeng Zhao School of Traffic and Transportation，Lanzhou Jiaotong University

The traffic congestion caused by the mismatch between the demand of car owners and the supply of parking spaces has become one of the severe traffic problems in various places. It is important to predict the remaining parking space which can not only help the driver to plan their trips reasonably but also reduce the pressure on urban road traffic. To reduce the stochastic fluctuations of complex data and improve the predictability of parking spaces， a hybrid prediction model EMD-LSTM-BiLSTM is proposed， which is combined the adaptive ability of empirical mode decomposition (EMD) to process time series data and the advantage of long short-term memory network (LSTM) and bidirectional long short-term memory network (BiLSTM) to solve long-range dependencies. first， the EMD algorithm is employed to decompose the components of different scales in the time series and generate a series of mode functions with the same characteristic scale. Next， the construction， training， and prediction of the LSTM-BiLSTM neural network are completed in the deep learning framework Keras. BiLSTM was built for proposing the bi-directional temporal features of the sequences， and LSTM was responsible for learning the output features， which effectively avoids large prediction errors. Finally， the performance of the model is verified by the actual parking data sets of different parking lots for parking space prediction. The proposed hybrid model is compared with a variety of current mainstream deep learning algorithms， and the effectiveness of the EMD-LSTM-BiLSTM method is validated. The results may provide some potential insights for parking prediction.

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