A bi-level programming for bus lane network design

Yu, Bin; Kong, Lu; Sun, Yao; Yao, Baozhen; Gao, Ziyou

doi:10.1016/j.trc.2015.02.014

Cited by 119 publications

(70 citation statements)

References 38 publications

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“…[19][20][21][22] As for the ship route planning and scheduling problem, many pure or MILP models and their extensions are developed, which can refer to these literatures. [23][24][25][26][27][28][29][30][31] Zeng and Yang 32 combined integer programming with heuristics. Cho and Perakis, 33 considering the fleet size, mixing, and delivery route assignment, presented integer linear programming models over a long-term planning horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet routing and scheduling problem based on constraints of chance

Guan

Peng

Chen

et al. 2017

Advances in Mechanical Engineering

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Tramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel's routes and schedule in the later period time, especially considering the situation that loading time window for a lot of cargoes has strong randomness. This article designed a linear programming model with chance constraints for the time window of loading cargo. Before the optimization, a survey for the waiting time of ships for berths is carried out in some of the ports with large export volume. Combined with the degree of acceptance how long ship owners can wait for the berth, the uncertain time window constraints can be transformed into deterministic constraints. The model is solved by column generation optimization technique. The model and algorithm are verified by a case of Panamax bulker fleet planning in real market. The results show that the model and the algorithm proposed in the article can well work on large-scale problem and can achieve good precision. Also, via sensitivity analysis, we provide decision makers good reference to balance profit and risks coming from randomness.

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Section: Literature Reviewmentioning

confidence: 99%

Fleet routing and scheduling problem based on constraints of chance

Guan

Peng

Chen

et al. 2017

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Due to its large-scale computation, problem-solving speed would have been slow if a direct search algorithm were applied. Thus, for an actual application, heuristic algorithm is often a first choice [32][33][34]. In particular, generation algorithm is applied to solve bus lane network design.…”

Section: Algorithm Design and Model Solutionmentioning

confidence: 99%

“…In particular, generation algorithm is applied to solve bus lane network design. In their paper, it can be that the generation algorithm is an effective method to solve this kind of line optimization [34]. Thus, the model is solved by a modified genetic algorithm based on the matrix coding method.…”

Section: Algorithm Design and Model Solutionmentioning

confidence: 99%

Optimization of Coastal Cruise Lines in China

Yang

Gao

Li³

2016

PROMET

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“…From the perspective of risk, an airport congestion risk forecasting model was proposed based on the random demand of airport approaches and departures. 15 The forecasting method based on air traffic flow models can obtain congestion status metrics using traffic flow parameter relation models, such as converging traffic flow model, [16][17][18] flight delay and cancelation model, 19 and approach flight instantaneous queuing model. 20 The forecasting method based on intelligent algorithms can obtain congestion status metrics by inputting forecasting values into a congestion status identification model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting traffic congestion status in terminal areas based on support vector machine

Zhang

Jiang

Yang

2016

Advances in Mechanical Engineering

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This article researches on a traffic congestion status forecasting method to improve the real-time monitoring and controlling of air traffic in terminal areas. First, a traffic congestion status evaluation method was introduced based on a fuzzy Cmeans clustering algorithm, as well as several traffic congestion status evaluation metrics. And then, a traffic congestion status forecasting model was proposed based on support vector machine. Finally, a real case study from a terminal area in China was provided to test and verify the proposed evaluation method and forecasting model. The evaluation results show that traffic congestion status of the terminal area can be classified into five levels: free, smooth, slightly congested, moderately congested, and severely congested. The forecasting results show that the mean absolute error and the cluster accuracy are 0.041% and 92.2%, respectively, which indicate that the forecasting model is very effective and accurate. In addition, it is also found that the parameters of forecasting period and size of training set have some influence on forecasting results, and the optimal results can be found when the two parameters values are 15 and 3, respectively.

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A bi-level programming for bus lane network design

Cited by 119 publications

References 38 publications

Fleet routing and scheduling problem based on constraints of chance

Fleet routing and scheduling problem based on constraints of chance

Optimization of Coastal Cruise Lines in China

Forecasting traffic congestion status in terminal areas based on support vector machine

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