Optimal Berth and Crane Combinations in Containerports

Schonfeld, Paul; Sharafeldien, Osama

doi:10.1061/(asce)0733-950x(1985)111:6(1060)

Cited by 35 publications

(15 citation statements)

References 7 publications

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“…The non-working energy consumption of a vessel handled by QCs mainly refers to the air conditioning energy consumption, the lighting energy consumption and other auxiliary equipment's energy consumption, which is mainly determined by the QCs' idle time, and the QCs' idle time is directly related to the number of QCs assigned to the vessel. Schonfeld and Sharafeldien [31] suggested that the work hours of n QCs assigned to a vessel was increased to n a times, where a denotes interference factor and 0 < a 6 1. But it is difficult to determine the deviation factor a.…”

Section: Energy Consumption Analysismentioning

confidence: 99%

Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving

2016

Advanced Engineering Informatics

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Section: Energy Consumption Analysismentioning

confidence: 99%

Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving

2016

Advanced Engineering Informatics

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“…Port operational capacities were modeled as a direct function of TEU storage space, ship berth space and number of cranes (Schonfeld and Sharafeldien, 1985;Alattar et al, 2006). Moreover, we added a fourth factor, port efficiency, given that ports with similar physical configurations and resources will have different throughputs (Turner et al, 2004;Cochrane, 2008).…”

Section: Characteristics Of Ports For Simulation Modelingmentioning

confidence: 99%

Modeling the effects of port disasters

Paul

Maloni

2010

Marit Econ Logist

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A b s t r a c t Weather or terrorism-related disasters at seaports can lead to significant economic losses from vessel and cargo delays. In times of such disasters, port capacities and optimal shipment routings would change rapidly, requiring near real-time analyses for planning response operations. To address this challenge, we offer a decision support system to help port networks analyze disaster response scenarios. As part of the model, an algorithm routes arriving ships to ports to optimize the use of network capacity with respect to ocean and inland transportation, port and inventory capital costs. To reflect changing port congestion conditions, port capacities are dynamically updated in the algorithm by regression-based parametric metamodels derived from the results of a simulation model. Demonstrating application through case studies of the North American container port network, we illustrate the magnitude of cost increases from disaster-based port shutdowns and highlight the practical need for coordinated, network-wide planning for disaster response.

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“…Schonfeld and Sharafeldien [7] and Huang et al [8,9] have derived expressions for measuring a ship's transfer time (the average operating time per ship). If the cranes could operate without mutual interference and if the workload was equally distributed among n cranes, Schonfeld and Sharafeldien [7] de®ned the transfer time as:…”

Section: The Modelmentioning

confidence: 99%

“…If the cranes could operate without mutual interference and if the workload was equally distributed among n cranes, Schonfeld and Sharafeldien [7] de®ned the transfer time as:…”

Section: The Modelmentioning

confidence: 99%

“…Gri ths [6], in calculating the optimal handling capacity at an iron ore terminal, found that the total handling capacity of a single berth working with two unloaders should account for mutual interference of equipment. Schonfeld and Sharafeldien [7] and Huang et al [8,9] also proposed a similar form of equation to measure the transfer time a ship spends at a terminal. In the equation, a mutual interference exponent was considered.…”

Section: Introductionmentioning

confidence: 99%

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Aggregates cranes handling capacity of container terminals: the port of Kaohsiung

Chu¹,

Huang²

2002

Maritime Policy & Management

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This study looks at the degrees of interference for multiple cranes that work simultaneously at six major container terminals in the port of Kaohsiung. The results con®rm that there are di erent degrees of mutual crane interference among terminals where di erent operational modes are adapted in the container yards. RTG handling systems show a very high value of aggregate cranes exponent f, with nearly no interference, as long as the number of allocated cranes is no more than the number of truck lanes under each crane. While SC systems reveal a relatively larger degree of mutual interference as the number of cranes increase.

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Optimal Berth and Crane Combinations in Containerports

Cited by 35 publications

References 7 publications

Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving

Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving

Modeling the effects of port disasters

Aggregates cranes handling capacity of container terminals: the port of Kaohsiung

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