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EN
In this work, we propose a multi-server queuing system for modeling the processes that occur in a maritime container terminal. In our study, the main operations that take place at the quay and in the yard are first disaggregated into several elementary activities. Then we propose the step-by-step calculation of the times of each operation that influences both the unloading and the loading of a container. Next, we analyze the vessel cycle time while separately investigating the STS (ship to shore) crane cycle time, the RTG (rubber tyred gantry) cycle time, as well as the IMV (internal movement vehicle) transfer time. Finally, we apply two process-driven simulation experiments to the system analysis. The paper demonstrates the proposed model’s effectiveness with data from the BCT Gdynia container terminal. We show that, among others, even with properly planned work of STS cranes and RTGs, there is still a high probability that the quay will become a bottleneck of the described processes.
EN
Nowadays managers and decision-makers around the world seek every opportunity to lower costs of the ship’s mooring time at seaports. In this article, main operations taking place at maritime container terminals are first disaggregated in several elementary activities. Then the vessel cycle time is analysed while separately investigating the STS (Ship to Shore) crane cycle time, the RTG (Rubber-Tyred Gantry) crane work cycle time and the IMV (Internal Movement Vehicle) transfer time. A triangular distribution describes times of each of the container handling stages while the PERT (Program Evaluation and Review Technique) method is used to estimate the total time for all reloading activities. The paper demonstrates the proposed method effectiveness with data of Baltic Container Terminal (BCT) Gdynia. The use of formulas developed for the calculation of times of individual operations that affect the reloading of a container at maritime container terminals enables an in-depth assessment of the effectiveness of the reloading processes. Thus, the proposed tool gives terminal managers opportunity to track which stage of the container reloading consumes most time and generates biggest costs.
EN
The article examines the competitiveness of small Baltic container terminals. In order to conduct the research, thirty terminals, whose annual maximum throughput capacity does not exceed 150,000 TEU, have been examined taking into consideration a number of criteria which are: length of the quay (C1), number of RTG (C2) and STS (C3) cranes, number of shortsea shipping connections (C4), maximum (technical) depth at the quay (C5), distance from motorways and expressways/national roads (C6), distance from the national railway station (C7). Selected k=7 criteria were used to perform PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluations II) multi-criteria ranking that enabled specifying those Baltic Sea container nodes which are in the area of strategic benefits for the analysed market sector.
PL
W referacie przedstawiono analizę zaników wolnozmiennych i szybkozmiennych w środowisku terminalu kontenerowego. Zaproponowano model analityczny parametrów rozkładów gęstości prawdopodobieństwa dla zaników, który umożliwia estymowanie wartości tych parametrów dla różnych uwarunkowań systemowych i środowiskowych. W ogólności dla zaników wolnozmiennych najlepiej dopasowany jest rozkład logarytmicznonormalny, natomiast dla zaników szybkozmiennych dobre dopasowanie do rozkładów empirycznych uzyskano dla rozkładów Rayleigha, Rice’a i Nakagamiego, przy czym ten ostatni jest najbardziej odpowiedni.
EN
In this paper a detailed analysis of slow and fast fading effects in container terminal environments is presented, fading distribution parameters are evaluated and an analytical model is proposed. The model is composed of a set of analytical equations allowing to evaluate fading statistical distribution parameters for different system and environments conditions. Globally, it is observed that for slow fading a good fitting is obtained with the Lognormal distribution. Fast fading effects are well modelled by Rayleigh, Rice and Nakagami distributions with the later one being the more appropriate.
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