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Parça ailesi oluşturmada endüktif-kaba kümeleme yaklaşımı

Inductive-rough sets appoach in part family formation

  1. Tez No: 153081
  2. Yazar: NUMAN ÇELEBİ
  3. Danışmanlar: PROF.DR. ORHAN TORKUL
  4. Tez Türü: Doktora
  5. Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
  6. Anahtar Kelimeler: Hücresel İmalat, Grup Teknoloji, Endüktif Öğrenme, Kaba Kümeleme, Veri Madenciliği Geleneksel üretim sistemleri, rekabetin çok fazla olmadığı
  7. Yıl: 2004
  8. Dil: Türkçe
  9. Üniversite: Sakarya Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 250

Özet

ÖZET

Özet (Çeviri)

INDUCTIVE-ROUGH SETS APPROACH IN PART FAMILY FORMATION SUMMARY Key Words : Cellular Manufacturing, Group Technology, Inductive Learning, Rough Sets Theory, Data Mining Traditional manufacturing systems are techniques that are developed for manufacturing environments that aren't surrounded by high competition (are under low competitive conditions), have low variety of products and long product life cycles. These systems have problems like long lead time, excessive work in process, complicated production planning and control and changing market demands. In order to solve these problems, many modern manufacturing systems which including new methods, techniques and manufacturing philosophies as MRP, OPT, GT, CM, JIT etc. had been improved. Cellular manufacturing is a modern production system known as application of GT in manufacturing. There have been many clustering techniques to form part families and machine cells in GT literature. Most of these techniques are inadequate to achieve expected results in industrial problems because that they had not been tested with real manufacturing data. For this reason, a flexible model, that determines the formation of the part families easily, based on the AI technologies (Inductive Learning and Rough Set Theory) and Data Mining, have been developed. This model, has applied on real manufacturing environment in four scenarios with different manufacturing characteristics. Later, a simulation model has been designed in order to make performance analysis of the each part families that belongs to these scenarios. The performance measures of the scenarios have been computed via this model and the differences of the scenarios have been tested by t-test in a determined significance level. The results of the comparison have been demonstrated graphically, and the effects of the manufacturing characteristics on forming part families have been analyzed. As a result it has been observed that operation time characteristic gives the most appropriate solutions in all similarity clusters and loading manners for all three performance measures. xx

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