Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/17397
Title: Hybrid genetic algorithm for 3D heterogeneous arbitrary sized bin packing problem with practical constraints
Researcher: Rajesh kanna S K
Guide(s): Sarukesi K
Keywords: 3D bin packing problem
Hybrid genetic algorithm
Mathematics
Upload Date: 11-Mar-2014
University: Anna University
Completed Date: 01/09/2013
Abstract: This research presents an algorithm which combines Genetic newlineAlgorithm (GA) with Tuning Algorithm (TA) for optimizing Three newlineDimensional (3D) packing of arbitrary sized heterogeneous bins into a newlinecontainer, by considering packing constraints namely placement constraint, newlineoverlapping constraint, stability constraint, weight constraint, load bearing newlineconstraint and orientation constraint. The main objective of this research is to newlineoptimally pack four different shapes of bins namely cube, rectangular prism, newlinecylinder and sphere of varying sizes into a container of standard dimension by newlinemeeting the packing constraints. GA has been used to minimize the unused newlineempty space inside the container by loading most of the heterogeneous bins newlineby satisfying the packing constraints. TA is a special heuristic algorithm newlinedeveloped in this research and has been used to enhance the genetic output by newlinefilling the remaining unused empty space inside the container. newline3D bin packing problem is to pack n number of bins into a newlinecontainer of standard dimension in such a way as to maximize the container newlinevolume utilization and inturn minimize the packing cost. Furthermore, bins to newlinebe packed can be of arbitrary sizes and of heterogeneous shapes. In this newlineresearch work, four different types of bin shapes taken were Cube, newlineRectangular prism, Cylinder and Sphere of arbitrary sizes. As a thumb rule, newlinecomplexity of a problem increases with increase in the number of parameters newlineinvolved in the problem, so the conventional optimization techniques do not newlineprovide satisfactory results all the time. In recent years, the researchers have newlinefocused towards the evolutionary approaches to obtain better solution for complex problems by meeting the constraints. Genetic Algorithm (GA) is one newlineof the evolutionary algorithms widely accepted for solving complex newlineoptimization problems.
Pagination: xvii, 179p.
URI: http://hdl.handle.net/10603/17397
Appears in Departments:Faculty of Mechanical Engineering

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01_title.pdfAttached File49.14 kBAdobe PDFView/Open
02_certificates.pdf2.83 MBAdobe PDFView/Open
03_abstract.pdf9.77 kBAdobe PDFView/Open
04_acknowledgement.pdf6.1 kBAdobe PDFView/Open
05_contents.pdf19.98 kBAdobe PDFView/Open
06_chapter 1.pdf37.04 kBAdobe PDFView/Open
07_chapter 2.pdf66.34 kBAdobe PDFView/Open
08_chapter 3.pdf16.83 kBAdobe PDFView/Open
09_chapter 4.pdf2.51 MBAdobe PDFView/Open
10_chapter 5.pdf21.4 kBAdobe PDFView/Open
11_appendix.pdf2.58 MBAdobe PDFView/Open
12_references.pdf43.07 kBAdobe PDFView/Open
13_publications.pdf11.34 kBAdobe PDFView/Open
14_vitae.pdf5.55 kBAdobe PDFView/Open


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