Univariate Throughput Forecasting Models on Container Terminal Equipment Planning

2014 ◽  
Vol 69 (7) ◽  
Author(s):  
Jonathan Yong Chung Ee ◽  
Abd Saman Abd Kader ◽  
Zamani Ahmad ◽  
Loke Keng Beng
Keyword(s):  
Time Horizon ◽  
Container Terminal ◽  
Empirical Method ◽  
Design Stage ◽  
Ratio Method ◽  
Technological Advancement ◽  
Planning Time ◽  
Optimum Operation ◽  
Forecasting Models ◽  
Terminal Equipment

Planning of Container Terminal equipment has always been uncertain due to seasonal and fluctuating throughput demand, along with factors of delay in operation, breakdown and maintenance. Many time-series models have been developed to forecast the unforeseen future of container throughput to project the needed amount of port equipments for optimum operation. Conventionally, a "ratio" method developed by port consultants at early port design stage is adopted for equipment planning, giving no consideration to the dynamic growth of the port in terms of improved layout and technological advancement in equipments. This study seeks first to enhance the empirical approach of the equipment planning at the end of planning time horizon by including assumed coefficient of port capacity parameters. The second is to compare the size of equipment purchase by receiving different terminal's future throughput demand from two univariate forecasting models at planning time horizon. The empirical method of equipment planning will be tested against the conventional yard equipment per quay crane ratio after deriving the throughput demand from forecasting models of Holt-Winter's exponential smoothing and seasonal ARIMA (autoregression integrated moving average) model. Results in the form of graphs and tables indicate similar forecasting pattern by two models and equipment estimation proofs to avail more redundancy for optimum operation. Suggestions for better estimation of equipments are also made for future models.

scholarly journals Numerical Analysis of the Rudder–Propeller Interaction

2020 ◽  
Vol 8 (12) ◽  
pp. 990
Author(s):  
Diego Villa ◽  
Andrea Franceschi ◽  
Michele Viviani
Keyword(s):  
Flow Problem ◽  
Empirical Method ◽  
Design Stage ◽  
Mutual Position ◽  
Flow Solver ◽  
Additional Information ◽  
Computational Fluid Dynamics Cfd ◽  
Semi Empirical ◽  
Insight Into ◽  
Numerical Approaches

The proper evaluation of the Rudder–Propeller interactions is mandatory to correctly predict the manoeuvring capability of a modern ship, in particular considering the commonly adopted ship layout (rudder often works in the propeller slipstream). Modern Computational Fluid Dynamics (CFD) solvers can provide, not only the performance of the whole system but also an insight into the flow problem. In the present paper, an open-source viscous flow solver has been validated against available literature experimental measurements in different conditions. After an extensive analysis of the numerical influence of the mesh arrangement and the turbulent quantities on the rudder provided forces, the study focused its attention on the forces generated by the rudder varying the propeller loading conditions and the mutual position between the two devices. These analyses give a hint to describe and improve a commonly-used semi-empirical method based on the actuator disk theory. These analyses also demonstrate the ability of these numerical approaches to correctly predict the interaction behaviour in pre-stall conditions with quite reasonable computational requests (proper also for a design stage), giving additional information on the sectional forces distribution along the span-wise rudder direction, useful to further develop a new semi-empirical rudder model.

Application of Artificial Neural Network Models to Activity Scheduling Time Horizon

2003 ◽  
Vol 1854 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Sean T. Doherty ◽  
Abolfazl Mohammadian
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Horizon ◽  
Travel Demand ◽  
Small Sample ◽  
Machine Learning Algorithms ◽  
Quebec City ◽  
Activity Type ◽  
Activity Scheduling ◽  
Planning Time

Machine-learning techniques are increasingly being applied in the areas of exploratory data analysis, prediction, and classification. At the same time that analytical techniques are expanding, new conceptual approaches to the modeling of travel are emerging in an effort to improve travel demand forecasting and better assess the impacts of emerging transportation policy. In particular, the shift toward activity-based travel analysis has led to the development of activity scheduling models. One of the key features of emerging models of this type is the attempt to simulate the order in which activities are added during a continuous process of schedule construction. In practice, a fixed order by activity type is often assumed; for example, work activities are planned first, followed by the planning of more discretionary activity types. By using observed data on the scheduling process from a small sample of households from Quebec City, Quebec, Canada, a neural network model that classifies activities according to the order in which they were planned, the planning time horizon (preplanned, planned, or impulsive), was developed. A variety of explanatory variables were used in the model related to individual-, household-, and activity-based characteristics such as spatial and temporal fixities. The model developed exhibited a relatively high degree of prediction with the test data, especially for the preplanned and impulsive categories of the planning time horizon. These results suggest that machine-learning algorithms could be used to predict the order in which activities are selected in emerging activity scheduling process models, thereby avoiding static assumptions related purely to activity type.

How Far in Advance Are Activities Planned?

2005 ◽  
Vol 1926 (1) ◽  
pp. 41-49 ◽  
Author(s):  
Sean T. Doherty
Keyword(s):  
Time Horizon ◽  
Survey Design ◽  
Extended Period ◽  
Simulation Models ◽  
Detailed Examination ◽  
Self Report ◽  
Activity Scheduling ◽  
Trip Planning ◽  
Planning Time ◽  
Time Horizons

The development of simulation models of activity-scheduling behavior has gained momentum over the past decade as a means to forecast travel demands. Of fundamental concern in these models is the process or timing of scheduling decisions–-or planning time horizon. Conceptually, it is understood that activities are planned over varying time horizons, but little empirical evidence exists. One way to explore these issues is to ask people to self-report when they planned their activities. However, this is a difficult question for researchers to formulate and for people to comprehend and recall, because people often plan (and replan) activity attributes over an extended period of time, some without much conscious thought. The objective of this paper is to describe the development of a planning time horizon query that was part of a larger activity scheduling process survey and to provide one of the first empirical analyses based on a random sample of 373 respondents. Included is a detailed examination of activity addition, modification, and trip-planning time horizons as well as analysis of “routine” and “unrecalled” decisions. Results indicate that people have the ability to recall a high level of detail on a planning time horizon, ranging from decisions made long ago that establish an initial skeleton schedule to continued preplanning in the days leading up to the event day and impulsive decisions made the day of the event. The implications of these results for future survey design and development of an activity-scheduling process simulation model are discussed.

A Petri Net Model and its Simulation for Straddle Carrier Direct-System Operation in a Container Terminal

2017 ◽  
Vol 862 ◽  
pp. 202-207 ◽  
Author(s):  
Putu Hangga Nan Prayoga ◽  
Takeshi Shinoda
Keyword(s):  
Petri Net ◽  
Performance Indicators ◽  
Container Terminal ◽  
Idle Time ◽  
System Operation ◽  
Direct System ◽  
Efficient Management ◽  
Terminal Performance ◽  
Gantry Cranes ◽  
Terminal Equipment

This paper presents a petri net model for examining effectiveness of straddle carrier direct-system operation. Dynamic of operation is addressed by formal petri net model that affiliates types of operation and sequences of motion for various agents in container terminal. An interchange model in the petri net is introduced to represent transloading process that controls the flow of containers between agents. In addition, simulation model was developed to examine the deployment scenarios of gantry cranes, straddle carriers and truck slots at transfer point. The results suggest terminal performance indicators such as level of productivity, waiting time and equipment’s idle time for each deployment scenarios which is beneficial as decision support systems for efficient management of terminal equipment.

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