Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/11433
Title: An adaptive cellular manufacturing system design under dynamic and stochastic production requirements using simulated annealing algorithm
Researcher: Jayakumar V
Guide(s): Raju, R.
Keywords: Cellular manufacturing system(CMS), stochastic production, annealing algorithm, cell formation problem, machine cells, part families
Upload Date: 23-Sep-2013
University: Anna University
Completed Date: 
Abstract: The ability to design and operate manufacturing facilities that can quickly and effectively adapt to changing technological and market requirements is becoming increasingly important to the success of any manufacturing organization. Cellular manufacturing system (CMS), which incorporates the flexibility of job shops and the high production rate of flow lines, has been recognized as a promising alternative to operate the manufacturing firms more efficiently and effectively. The first and most significant step in designing a CMS is to identify independent machine cells (MCs) and part families (PFs), and to assign them to each other with minimum material movements and associated costs. This is known as a cell formation problem (CFP). The effectiveness of the CMS is sensitive to fluctuations in product demand and product mix. The main objectives of the proposed research are to formulate a multi-objective mixed-integer non-linear comprehensive mathematical model for a CFP in dynamic and stochastic production requirements, addressing simultaneously various reallife production parameters; and develop an efficient solution methodology for solving the developed integrated model. In this research, a new approach called adaptive design strategy is developed to design a CMS that responds to changing product mix and/or demand in future periods by rearranging the current manufacturing system. An experimental design is performed to validate the developed SA using the same data used to validate the mathematical programming model and a performance comparison is also made between SA and optimal solutions with respect to solution quality and computational effort. The results show that the SA solution procedure provides good quality solutions for real-life size problems within acceptable computational time. An industrial case study is also presented to illustrate the applicability of the developed model and the solution methodology. newline newline newline
Pagination: xxii, 209
URI: http://hdl.handle.net/10603/11433
Appears in Departments:Faculty of Mechanical Engineering

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01_title.pdfAttached File50.47 kBAdobe PDFView/Open
02_certificates.pdf1.11 MBAdobe PDFView/Open
03_abstract.pdf20.63 kBAdobe PDFView/Open
04_acknowledgement.pdf14.92 kBAdobe PDFView/Open
05_contents.pdf56.62 kBAdobe PDFView/Open
06_chapter 1.pdf315.52 kBAdobe PDFView/Open
07_chapter 2.pdf116.61 kBAdobe PDFView/Open
08_chapter 3.pdf31.37 kBAdobe PDFView/Open
09_chapter 4.pdf198.73 kBAdobe PDFView/Open
10_chapter 5.pdf166.2 kBAdobe PDFView/Open
11_chapter 6.pdf424.83 kBAdobe PDFView/Open
12_chapter 7.pdf33.06 kBAdobe PDFView/Open
13_appendices 1 to 3.pdf230.28 kBAdobe PDFView/Open
14_references.pdf56.29 kBAdobe PDFView/Open
15_publications.pdf17.82 kBAdobe PDFView/Open
16_vitae.pdf13.07 kBAdobe PDFView/Open


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