Tide is a significant factor which interferes with the berthing and departing operations of vessels in tidal ports. It is a preferable way to incorporate this factor into the simultaneous berth allocation and quay cra...Tide is a significant factor which interferes with the berthing and departing operations of vessels in tidal ports. It is a preferable way to incorporate this factor into the simultaneous berth allocation and quay crane( QC) assignment problem( BACAP) in order to facilitate the realistic decision-making process at container terminal. For this purpose,an integrated optimization model is built with tidal time windows as forbidden intervals for berthing or departing. A hind-and-fore adjustment heuristic is proposed and applied under an iterative optimization framework. Numerical experiment shows the satisfying performance of the proposed algorithm.展开更多
In Container terminals,a quay crane’s resource hour is affected by various complex nonlinear factors,and it is not easy to make a forecast quickly and accurately.Most ports adopt the empirical estimation method at pr...In Container terminals,a quay crane’s resource hour is affected by various complex nonlinear factors,and it is not easy to make a forecast quickly and accurately.Most ports adopt the empirical estimation method at present,and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance.Through the ensemble learning(EL)method,the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data.A multi-factor ensemble learning estimation model based quay crane’s resource hour was established.Through a numerical example,it is finally found that Adaboost algorithm has the best effect of prediction,with an error of 1.5%.Through the example analysis,it comes to a conclusion:the error is 131.86%estimated by the experience method.It will lead that subsequent shipping cannot be serviced as scheduled,increasing the equipment wait time and preparation time,and generating additional cost and energy consumption.In contrast,the error based Adaboost learning estimation method is 12.72%.So Adaboost has better performance.展开更多
This paper proposes a mixed integer programming model for the allocation of rail mounted gantry cranes for four basic yard activities with different priorities. The model pays special attention to the typical features...This paper proposes a mixed integer programming model for the allocation of rail mounted gantry cranes for four basic yard activities with different priorities. The model pays special attention to the typical features of this kind of gantry cranes, such as a restricted traveling range and a limited number of adjustments during loading and discharging operations. In contrast to most of the literature dealing with these four yard activities individually, this paper models them into an integrated problem, whose computational complexity is proved to be NP-hard. We are therefore motivated to develop a Lagrangian relaxation-based heuristic to solve the problem. We compare the proposed heuristic with the branch-and-bound method that uses commercial software packages. Extensive computational results show that the proposed heuristic achieves competitive solution qualities for solving the tested problems.展开更多
基金National Natural Science Foundations of China(Nos.70771065,71171130,61473211,71502129)
文摘Tide is a significant factor which interferes with the berthing and departing operations of vessels in tidal ports. It is a preferable way to incorporate this factor into the simultaneous berth allocation and quay crane( QC) assignment problem( BACAP) in order to facilitate the realistic decision-making process at container terminal. For this purpose,an integrated optimization model is built with tidal time windows as forbidden intervals for berthing or departing. A hind-and-fore adjustment heuristic is proposed and applied under an iterative optimization framework. Numerical experiment shows the satisfying performance of the proposed algorithm.
文摘In Container terminals,a quay crane’s resource hour is affected by various complex nonlinear factors,and it is not easy to make a forecast quickly and accurately.Most ports adopt the empirical estimation method at present,and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance.Through the ensemble learning(EL)method,the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data.A multi-factor ensemble learning estimation model based quay crane’s resource hour was established.Through a numerical example,it is finally found that Adaboost algorithm has the best effect of prediction,with an error of 1.5%.Through the example analysis,it comes to a conclusion:the error is 131.86%estimated by the experience method.It will lead that subsequent shipping cannot be serviced as scheduled,increasing the equipment wait time and preparation time,and generating additional cost and energy consumption.In contrast,the error based Adaboost learning estimation method is 12.72%.So Adaboost has better performance.
基金supported by the National Nature Science Foundation of China under grant numbers 71102011and 51105394Guangdong provincial department of science and technology(Number 2011B090400384)
文摘This paper proposes a mixed integer programming model for the allocation of rail mounted gantry cranes for four basic yard activities with different priorities. The model pays special attention to the typical features of this kind of gantry cranes, such as a restricted traveling range and a limited number of adjustments during loading and discharging operations. In contrast to most of the literature dealing with these four yard activities individually, this paper models them into an integrated problem, whose computational complexity is proved to be NP-hard. We are therefore motivated to develop a Lagrangian relaxation-based heuristic to solve the problem. We compare the proposed heuristic with the branch-and-bound method that uses commercial software packages. Extensive computational results show that the proposed heuristic achieves competitive solution qualities for solving the tested problems.
