A hybrid algorithm for Urban transit schedule optimization

Tang, Jinjun; Yang, Yifan; Qi, Yong

doi:10.1016/j.physa.2018.08.017

Cited by 43 publications

(13 citation statements)

References 25 publications

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“…Finally, a practical MSDEDM example of highway emergency is given to illustrate the effectiveness and practicability of the proposed dynamic fuzzy approach. Remarkably, the application implies that the fuzzy theory can well handle the uncertainty of real world in practice and have wide application prospect in other related fields, such as public transport [36][37][38], logistics network [39][40][41], and traffic management [42]. e method of the dynamic decision-making procedure would be a useful reference to the similar decision-making researches.…”

Section: Resultsmentioning

confidence: 99%

A Multistage Dynamic Emergency Decision-Making Method considering the Satisfaction under Uncertainty Information

Liu

2021

Journal of Advanced Transportation

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Emergency decision-making (EDM) is of paramount importance, especially when the emergency occurs. The evolution nature of the emergency, such as multistage, uncertainty, dynamic, and information updating, has been playing a key role in the dynamic emergency decision-making process. However, most existing studies ignored the aforementioned nature. Our approach accounts for the dynamics inherent to a real emergency decision-making process and presents a multistage dynamic emergency decision-making (MSDEDM) procedure of a dynamic programming model based on decision-makers’ psychological reference satisfactory degree. Firstly, interval-valued trapezoidal intuitionistic fuzzy numbers (IVTrIFNs) are used to depict the relevant fuzziness and uncertainty of information. Secondly, by considering the dynamic evolution process of emergency and the decision-makers’ psychological reference expectation effect, the principle of MSDEDM approach is presented. Based on the analysis, the dynamic model on the new psychological reference satisfactory parameter formula is presented to obtain the optimal satisfaction and weight of each stage. Then, the value utility function based on the DMs’ risk attitude is proposed to obtain the comprehensive value of each emergency alternative for each stage and achieve the ranking results of each stage. Furthermore, a case study involving the transportation emergency decision-making problem demonstrates that the proposed method can achieve selection of the optimal alternatives for each stage, as well as adjustment of the alternatives for neighbouring stages. Finally, the comparative analysis and sensitivity analysis for the results are used to further verify the feasibility and practicability of the proposed method.

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Section: Resultsmentioning

confidence: 99%

A Multistage Dynamic Emergency Decision-Making Method considering the Satisfaction under Uncertainty Information

Liu

2021

Journal of Advanced Transportation

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“…For the multiple random phenomena involved in the order picking process, further modelling and analysis are needed. The greedy algorithm and genetic algorithm are commonly used to solve such problems; however, different algorithms can also be employed, such as the hybrid of genetic algorithm and simulated annealing algorithm [ 17 ]. The objective is to estimate the order picking time under the different picking rules to explore the nature of the problem and compare the characteristics of the different algorithms.…”

Section: Research Status Of Dense Mobile Rack Storage Systemsmentioning

confidence: 99%

Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

2021

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The utilization of a storage space can be considerably improved by using dense mobile racks. However, it is necessary to perform an optimisation study on the order picking to reduce the time cost as much as possible. According to the channel location information that needs to be sorted, the multiple orders are divided into different batches by using hierarchical clustering. On this basis, a mathematical model for the virtual order clusters formed in the batches is established to optimize the order cluster picking and rack position movement, with the minimum picking time as the objective. For this model, a hybrid genetic algorithm is designed, and the characteristics of the different examples and solution algorithms are further analysed to provide a reference for the solution of the order picking optimisation problem in a dense mobile rack warehouse.

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“…e development trend prediction model of network recurrent tra c congestion can be constructed by using a deep for people to reveal the evolution law of tra c congestion and scienti cally control tra c ow [82][83][84][85][86][87][88][89][90][91].…”

Section: Using Deep Learning Eory To Predict the Development Trend Ofmentioning

confidence: 99%

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Zhou

et al. 2020

Journal of Advanced Transportation

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To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

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A hybrid algorithm for Urban transit schedule optimization

Cited by 43 publications

References 25 publications

A Multistage Dynamic Emergency Decision-Making Method considering the Satisfaction under Uncertainty Information

A Multistage Dynamic Emergency Decision-Making Method considering the Satisfaction under Uncertainty Information

Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

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