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
An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithm was designe...An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithm was designed. In the QCs scheduling phase of the algorithm, a search was performed to determine a good QC unloading operation order. For each QC unloading operation order generated during the QC's scheduling phase, another search was run to obtain a good yard trailer routing for the given QC's unloading order. Using this information, the time required for the operation was estimated, then the time of return to availability of the units was fed back to the QC scheduler. Numerical tests show that the two-phase Tabu Search algorithm searches the solution space efficiently, decreases the empty distance yard trailers must travel, decreases the number of trailers needed, and thereby reduces time and costs and improves the integration and reliability of container terminal operation systems.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.
文摘An optimization model for scheduling of quay cranes (QCs) and yard trailers was proposed to improve the overall efficiency of container terminals. To implement this model, a two-phase tabu search algorithm was designed. In the QCs scheduling phase of the algorithm, a search was performed to determine a good QC unloading operation order. For each QC unloading operation order generated during the QC's scheduling phase, another search was run to obtain a good yard trailer routing for the given QC's unloading order. Using this information, the time required for the operation was estimated, then the time of return to availability of the units was fed back to the QC scheduler. Numerical tests show that the two-phase Tabu Search algorithm searches the solution space efficiently, decreases the empty distance yard trailers must travel, decreases the number of trailers needed, and thereby reduces time and costs and improves the integration and reliability of container terminal operation systems.
