Delft University of Technology
Faculty Mechanical, Maritime and Materials Engineering
Transport Technology

M.E. van Haersma Buma Influence of Stochastics on Crane Cycle Times
Computer program, Report 2008.TEL.7273, Transport Engineering and Logistics.

Over the past fifty years, quay cranes have evolved into large structures with a variety of drives and features. During the past years, there seems to have been a permanent desire for faster and more powerful cranes in order to decrease crane cycle time, without serious evidence to support this desire. In this report the influence of stochastics on crane cycle times is studied through development of a model and experiments with this model.

Crane cycle time comprises two types of elements: deterministic and stochastic. The first concerns actual movement of the spreader while the latter represent delays such as attachment of Semi Automated Twist Locks (SATL's) to the spreader and time required for the correction of load sway. Each of these delays is described in detail as well as the way in which it is implemented into the model.

9 different experiments are carried out with 3 different ship configurations (27 experiments in total). Each configuration has a specific amount of containers to be (un)-loaded on deck and in the hull of the ship. These experiments concern the following alterations to deterministic and stochastic parameters:

 Experiment nr.   Deterministic  Parameters
1  Standard ac/deceleration and speed   Standard stochastic parameters 
2  High speed & fast ac/deceleration  Standard stochastic parameters
3  Low speed & slow ac/deceleration  Standard stochastic parameters
4  Standard ac/deceleration and speed  Short stochastic delays
5  Standard ac/deceleration and speed  Long stochastic delays
6  High speed & fast ac/deceleration  Short stochastic delays
7  Low speed & slow ac/deceleration  Long stochastic delays
8  High speed & fast ac/deceleration  Long stochastic delays
9  Low speed & slow ac/deceleration
 Short stochastic delays

Based on the experiments and the input parameters of the model that were used, it can be concluded that the influence of stochastics on crane cycle time is substantial; its share of average cycle time is 20% to 55% depending on the model and ship configuration. Applying alterations to deterministic or stochastic parameters leads to a change in average cycle time that is dependent on the contribution of the respective parameter to this average cycle time.

The impact of parameter changes on the standard deviation of the average cycle time is significantly larger for stochastic alterations than for deterministic alterations. Improving the stochastic parameters (short stochastic delays), leads to a decrease of up to 25% of the standard deviation.

Combining the two types of alterations leads to changes that are dependent on the configuration of the ship, i.e. its size and ratio of deck-to-hull moves. Hull cycles are generally more sensitive to deterministic alterations as they require more spreader travel with respect to deck moves. SATL-attachment makes deck cycles more sensitive to stochastic alterations.

It is concluded that investments in quay crane productivity should be based on the goal of the terminal operator. More predictably cycle times (less variance) can be achieved best by improvement of stochastic parameters while shorter cycle times are achieved through improvement of deterministic parameters. Most importantly, a thorough assessment of the actual terminal situation should be done before investing in improvement of either type of parameter. This will give insight into the contributions of the different cycle time elements as well as average ship sizes and average number of deck and hull moves per ship.

In order to obtain additional insights in the influence of stochastics elements on crane cycle time, it is recommended that more experiments are carried out where alterations are applied to a single (instead of multiple) stochastic parameter. This will provide more information on which stochastic elements are of most influence on crane cycle time. The model can also be expanded to take additional terminal operations such as AGV movements and yard stacking into account; this can facilitate a better optimization of the processes at a container terminal.

Reports on Transport Engineering and Logistics (in Dutch)
Modified: 2008.09.08; , TU Delft / 3mE / TT / LT.