Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/48813
Title: Prediction of yard crane operation sequence and vehicle departure times at a yard block in container terminals
Authors: Pang, Pangianto
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Physical sciences and engineering
Issue Date: 2012
Abstract: There are three main types of container handling equipment in container terminal; Quay Cranes (QC), vehicles and Yard Cranes (YC). Their operations are highly inter-dependent. An optimal coordination of between these equipments can make the terminal operating like an whole machine as these operation can smoothly work with each other. The overall objective of the terminal process is to maximizing the berth utilization by minimizing the total tardiness of jobs. By Guo, Huang, Hsu and Low (2011), they proposed to divide the inter-dependent processes into two cyclic processes; YC dispatching and vehicle dispatching. Given the jobs deadline by terminal gate system, the vehicle dispatching subsystem will be able to dispatching vehicle to YCs and QCs. In the other word, the vehicle dispatching subsystem will supply the expected vehicle arrival time to YC dispatching subsystem. With the given expected vehicle arrival time, the YCs will be able to schedule a sequence to serve the jobs which has minimum average tardiness. Subsequently, the YC dispatching system will be able to supply back the expected time when the vehicle can leave the YC (the job completion time) to vehicle dispatching subsystem. With the predicted arrival time and deadlines of jobs which are given by vehicle dispatching subsystem, YC dispatching subsystem will sequence out jobs to be served based on heuristic and optimal algorithms. Heuristic algorithms provides a good estimation to the optimal solution and can give an solution in a short time. On the other hand, optimal algorithms can give an optimal solution but search space is in high complexity. To cut down the search space, an admissible heuristic is used. Recursive backtracking A* (RBA) is proven to be the best algorithm by Guo, Huang, Hsu and Low (2011). It is an optimal algorithm which can give optimal solution in a short time as it will prunes away not promising nodes. The RBA can also be prioritize some entity in the algorithm. However, the actual arrival time of vehicle may be different from the predicted arrival time due to the uncertainty conditions of the vehicle journey. Experimental results shows that RBA performs fairly well in the circumstances of uncertainty. Even in the circumstances of high uncertainty, RBA shows a consistent result than other algorithms. Uncertainty may cause loss performance in planning. Loss performance means the vehicle might not be able to leave the YC accurately at the predicted completion time of job. Experiments are done in this project and the result shows that the loss performance will not accumulate over jobs in a planning window. We want to have an estimation of actual completion time of job given by the predicted arrival time. Statistical model which is the result of experiments, is used to produce a linear regression line. The linear regression line formula is used to provide the estimation. The estimation has low computation cost but it has a standard error as the trade-off.
URI: http://hdl.handle.net/10356/48813
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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