OPTIMAL DOCK BLOCK ARRANGEMENT CONSIDERING SUBSEQUENT SHIPS SOLVED BY GENETIC ALGORITHM

Author(s):  
Chen Chen ◽  
◽  
Kim Huat Chua ◽  
2007 ◽  
Vol 23 (01) ◽  
pp. 30-35
Author(s):  
Duck Young Yoon ◽  
Ranjan Varghese

The major problem arises at a shipyard because of scarcity of space for arranging the building blocks of a ship under construction. A standardized erection sequence diagram is generally available to provide the prioritized erection sequence, and it serves as the framework. In order to make a timely erection of the blocks, a post plan has to be developed so that the blocks lie in the nearest possible vicinity of the material-handling devices while keeping the priority of erection, and the blocks are arranged in the pre-erection area. The genetic algorithm based solution procedure is developed to produce the optimal spatial arrangement. An innovative algorithm nicknamed ISBAS (Intelligent Spatial Block Arrangement Scheduler) has been developed and implemented using genetic algorithm, and a workable computer program has been developed using VC++. The detailed genetic operation is performed to run to meet the objectives of the critically complicated problem. Also, hand-in-hand algorithms such as knapsack problem, traveling salesmen problem, and weight assignment algorithms are used to segregate the better options. An indigenously developed looking-forward algorithm is also in place.


1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan

Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.


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