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ÇOK AŞAMALI PROSESLERDE ÖRNEK HACMİNİN BELİRLENMESİÜZERİNE BİR MODEL VE GENETİK ALGORİTMALAR YARDIMIYLA ÇÖZÜM ÖNERİSİ

Yıl 2006, Sayı: 15, 435 - 456, 01.02.2006

İhsan Kaya

Öz

Bu çalışmada, çok aşamalıkabul örneklemesi problemleri için Genetik Algoritma GA yaklaşımıincelenmiştir. Langner 2001 tarafından geliştirilen model kullanılarak, çok aşamalımuayene probleminin çözümüne ilişkin Visual Basic 6.0 programlama dilinde bir program hazırlanmışve GA ile çözülen bu modelden elde edilen sonuçlar ANSI/ASQC Z1.4 örnekleme planıile karşılaştırılmıştır. Her iki örnekleme planıiçin elde edilen örnek hacmi n ve kabul edilebilir kusur sayısı c değerleri için çalışma karakteristiği OC-Operating Characteristics ve Kabul Olasılığı Pa eğrileri WinQSB yardımıyla çizilerek sonuçlar karşılaştırılmıştır.

Anahtar Kelimeler

Kabul Örneklemesi Genetik Algoritma Çok Aşamalı Prosesler

Kaynakça

  • BAI, D.S., HONG, S.H. 1990. “Economic Design of Sampling Plans with Multi-Decision Alternatives”, Naval Research Logisitcs, 37, 905-918
  • BEBBINGTON, M., LAI, C.D., GOVINDARAJU, K., 2003. “Continuous sampling plans for Markov- dependent production processes under limited inspection capacity”. Mathematical and Computer Modelling, 38, 1137-1145
  • CHAKRABORTY, T.K. 1994. “A class of single sampling inspection plans based on possibilistic programming problem”. Fuzzy Sets and Systems, 63, 35-43
  • CHENG, R., GEN, M., TSUJIMURAY, Y. 1999. “A Tutorial Survey of Job Shop Scheduling Problems Using Genetic Algorithms, Part II: Hybrid Genetic Search Strategies”. Computers and Industrial Engineering 36, 343-364
  • ENGİN, O., 2001. Akış Tipi Çizelgeleme Problemlerinin Genetik Algoritma ile Çözüm Performansının Artırılmasında Parametre Optimizasyonu, İ.T.Ü., Fen Bilimleri Enstitüsü, Doktora Tezi, İstanbul
  • EVANS, G.W., ALEXANDER, S.M. 1987. “Multiobjective Decision Analysis for Acceptance Sampling Plans”. IEE Transactions, Vol. 19, No:3, 308- 316
  • FERRELL J., W.G., CHHOKER, A., 2002. “Design of economically optimal acceptance plans with inspection error”. Computers & Operations Research, 29, 1283-1300
  • FIĞLALI, A. , ENGİN, O. 2002. “Genetik Algoritmalarla Akış Tipi Çizelgelemede Üreme Yöntemi Optimizasyonu”. İTÜ Dergisi, s. 1-6.
  • FINK, R.L., MARGAVIO, T.M. 1994. “Economic Models for Single Sample Acceptance Sampling Plans, No Inspection, and 100 Percent Inspection”. Decision Sciences, vol 25, no 4
  • FU, H.H., TSAI, H.T., LIN, C.W., WEI, D. 2004. “Application of a single sampling plan for auditing medical-claim payments made by Taiwan natioanl haelth insurance”. Health Policy, Article in Press
  • GEN, M., CHENG, R. 1999. Genetic Algorithms & Engineering Optimization, John Wiley & Sons Inc.
  • GOLDBERG, D.E., 1989. Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Publishing Company, USA
  • GÖZLÜ, S. 1990. Endüstriyel Kalite Kontrolü. Teknik Üniversite Matbaası, İstanbul
  • HASSAN, M. Z. 1985. Analysis of Manufacturing and Quality Systems Using Simulation, Engineering Costs and Production Economics, 9, 33-40
  • HUANG, W.T., LIN, Y.P., 2004. “Bayesian sampling plans for exponential distribution based on uniform random censored data”. Computational Statistics & Data Analysis, 44, 669-691
  • JANG, J.S.R., 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Chapter 7: Derivative-Free Optimization, Prentice-Hall, s. 173-196, USA
  • JARAIEDI, M., SEGALL, R.S. 1990. “Mathematical modelling of Dodge- Romig sampling plans for random incoming quality”. Appl. Math. Modelling, Vol.14, May
  • KAYA, İ., ENGİN, O., 2004. “Proses Kontrol Diyagramları Kullanımında Örnek Hacmi Belirleme Probleminin Genetik Algoritma İle Çözümünde Uygun Çaprazlama Yöntemi Ve Oranının Belirlenmesi”, IV. Ulusal Üretim Araştırmaları Sempozyumu 8-10 Ekim Konya, 4, 1, 347 - 353, 2004
  • KLEIJNEN, J.P.C., KRIENS, J., LAFLEUR, M.C.H.M., PARDOEL, J.H.F. 1992. “Sampling for quality inspection and correction: AOQL performance criteria”. European Journal of Operational Research, 62, 372-379
  • KOBILINSKY, A., BERTHEAU, Y. 2005. “Minimum Cost Acceptance Sampling Plans for Grain Control, with Application to GMO Detection”. Journal of Statistical Planning and Inference, 132, 149-162
  • KOUIKOGLU, V.S., 1994. “Single Sampling Plans for Attributes Satisfying an Arbitrary Set of Constarints- A Graphical Approach”. Microelectronics and Reliability, vol. 34, No:6, 1071-1077
  • KURT, M., SEMETAY, C., 2001. Genetik Algoritma ve Uygulama Alanları, Mühendis ve Makine, sayı 501
  • LANGNER, A. H. 2001. Genetic Algorithms in Quality Control Problems, Ph.D. Thesis, Arizona State University, December 2001
  • LANGNER, H.A., MONTGOMERY, D.C., CARLYLE, W.M. 2002. “Solving a Multistage Partial Inspection Problem Using Genetic Algorithms”. International Journal of Production Research, Vol. 40, No. 8, 1923-1940
  • LAWRENCE, D.,1990. Handbook of Genetic Algorithms, Addison Wesley
  • LEE, J., UNNIKRISHNAN, S., 1998. “Planning Quality Inspection Operations in Multi-stage Manufacturing Systems with Inspection Errors”, International Journal of Production Research, 36, 141-155
  • MARKOWSKI, E.P., MARKOWSKI, C.A., 2002. “Improved attribute acceptance sampling plans in the presence of misclassification error”. European Journal of Operational Research, 139, 501-510
  • MURATA, T., ISHIBUCHI, H., TANAKA, H., 1996a. “Genetic Algorithms for Flow Shop Scheduling Problems”. Computers and Industrial Engineering vol.30, No.4, pp 1061-1071
  • MURATA, T., ISHIBUCHI, H., TANAKA, H., 1996b. “Multi-Objective Genetic Algorithms and Its Applications to Flow Shop Scheduling”. Computers and Industrial Engineering, vol 30, No 4, pp 957-968
  • PARKINSON, D.B. 1988. “Optimum Sampling Plans Based On Post- Quality Control Reliability”, Reliability Engineering and System Safety. 21, 59-75
  • PEARN, W. L., WU, C.W. 2005. “An Effective Decision Making Method for Product Acceptance”. Omega, Article in Press
  • RONEN, B., SPECTOR, Y. 1995. Evaluating Sampling Strategy Under Two Criteria, European Journal of Operational Research, 80, 59-67
  • SOHN, S.Y., JANG, J.S., 2001. Acceptance sampling based on reliability degradation data, Reliability Engineering and System Safety, 73, 67-72
  • TAGARAS, G., LEE, H.L., 1987. “Optimal Bayesian Single Sampling Attributes Plans with Modified Beta Prior Distribution”, Naval Research Logisitics, 34, 789-801
  • TAGARAS, G., 1994. Economic Acceptance Sampling By Variables With Quadratic Quality Costs, IIE Transactions, vol 26, no 6
  • WALL, M.S., ELSHENNAWY, A.K. 1989. “Economically Based Acceptance Sampling Plans”, Computers Industrial Engineering, 17,340-346

Yıl 2006, Sayı: 15, 435 - 456, 01.02.2006

İhsan Kaya

Öz

In this study, genetic algorithms GAs approach was investigated for problems of acceptance sampling in multistage processes. A computer program which was coded Visual Basic Computer Programming Language 6.0 was prepared to solve of multistage inspection problems help of a model which was improved by Langner 2001 and results of this model which was solved by GAs were compared results of ANSI/ASQC Z1.4 Acceptance Sampling Standards. Operating Characteristics OC and Acceptance Probability Pa diagrams were created depend on sampling number, n, and acceptance number, c, help of WinQSB program and theirs results were analyzed

Anahtar Kelimeler

Acceptance Sampling Genetic Algorithms Multi-stage Processes

Kaynakça

  • BAI, D.S., HONG, S.H. 1990. “Economic Design of Sampling Plans with Multi-Decision Alternatives”, Naval Research Logisitcs, 37, 905-918
  • BEBBINGTON, M., LAI, C.D., GOVINDARAJU, K., 2003. “Continuous sampling plans for Markov- dependent production processes under limited inspection capacity”. Mathematical and Computer Modelling, 38, 1137-1145
  • CHAKRABORTY, T.K. 1994. “A class of single sampling inspection plans based on possibilistic programming problem”. Fuzzy Sets and Systems, 63, 35-43
  • CHENG, R., GEN, M., TSUJIMURAY, Y. 1999. “A Tutorial Survey of Job Shop Scheduling Problems Using Genetic Algorithms, Part II: Hybrid Genetic Search Strategies”. Computers and Industrial Engineering 36, 343-364
  • ENGİN, O., 2001. Akış Tipi Çizelgeleme Problemlerinin Genetik Algoritma ile Çözüm Performansının Artırılmasında Parametre Optimizasyonu, İ.T.Ü., Fen Bilimleri Enstitüsü, Doktora Tezi, İstanbul
  • EVANS, G.W., ALEXANDER, S.M. 1987. “Multiobjective Decision Analysis for Acceptance Sampling Plans”. IEE Transactions, Vol. 19, No:3, 308- 316
  • FERRELL J., W.G., CHHOKER, A., 2002. “Design of economically optimal acceptance plans with inspection error”. Computers & Operations Research, 29, 1283-1300
  • FIĞLALI, A. , ENGİN, O. 2002. “Genetik Algoritmalarla Akış Tipi Çizelgelemede Üreme Yöntemi Optimizasyonu”. İTÜ Dergisi, s. 1-6.
  • FINK, R.L., MARGAVIO, T.M. 1994. “Economic Models for Single Sample Acceptance Sampling Plans, No Inspection, and 100 Percent Inspection”. Decision Sciences, vol 25, no 4
  • FU, H.H., TSAI, H.T., LIN, C.W., WEI, D. 2004. “Application of a single sampling plan for auditing medical-claim payments made by Taiwan natioanl haelth insurance”. Health Policy, Article in Press
  • GEN, M., CHENG, R. 1999. Genetic Algorithms & Engineering Optimization, John Wiley & Sons Inc.
  • GOLDBERG, D.E., 1989. Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Publishing Company, USA
  • GÖZLÜ, S. 1990. Endüstriyel Kalite Kontrolü. Teknik Üniversite Matbaası, İstanbul
  • HASSAN, M. Z. 1985. Analysis of Manufacturing and Quality Systems Using Simulation, Engineering Costs and Production Economics, 9, 33-40
  • HUANG, W.T., LIN, Y.P., 2004. “Bayesian sampling plans for exponential distribution based on uniform random censored data”. Computational Statistics & Data Analysis, 44, 669-691
  • JANG, J.S.R., 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Chapter 7: Derivative-Free Optimization, Prentice-Hall, s. 173-196, USA
  • JARAIEDI, M., SEGALL, R.S. 1990. “Mathematical modelling of Dodge- Romig sampling plans for random incoming quality”. Appl. Math. Modelling, Vol.14, May
  • KAYA, İ., ENGİN, O., 2004. “Proses Kontrol Diyagramları Kullanımında Örnek Hacmi Belirleme Probleminin Genetik Algoritma İle Çözümünde Uygun Çaprazlama Yöntemi Ve Oranının Belirlenmesi”, IV. Ulusal Üretim Araştırmaları Sempozyumu 8-10 Ekim Konya, 4, 1, 347 - 353, 2004
  • KLEIJNEN, J.P.C., KRIENS, J., LAFLEUR, M.C.H.M., PARDOEL, J.H.F. 1992. “Sampling for quality inspection and correction: AOQL performance criteria”. European Journal of Operational Research, 62, 372-379
  • KOBILINSKY, A., BERTHEAU, Y. 2005. “Minimum Cost Acceptance Sampling Plans for Grain Control, with Application to GMO Detection”. Journal of Statistical Planning and Inference, 132, 149-162
  • KOUIKOGLU, V.S., 1994. “Single Sampling Plans for Attributes Satisfying an Arbitrary Set of Constarints- A Graphical Approach”. Microelectronics and Reliability, vol. 34, No:6, 1071-1077
  • KURT, M., SEMETAY, C., 2001. Genetik Algoritma ve Uygulama Alanları, Mühendis ve Makine, sayı 501
  • LANGNER, A. H. 2001. Genetic Algorithms in Quality Control Problems, Ph.D. Thesis, Arizona State University, December 2001
  • LANGNER, H.A., MONTGOMERY, D.C., CARLYLE, W.M. 2002. “Solving a Multistage Partial Inspection Problem Using Genetic Algorithms”. International Journal of Production Research, Vol. 40, No. 8, 1923-1940
  • LAWRENCE, D.,1990. Handbook of Genetic Algorithms, Addison Wesley
  • LEE, J., UNNIKRISHNAN, S., 1998. “Planning Quality Inspection Operations in Multi-stage Manufacturing Systems with Inspection Errors”, International Journal of Production Research, 36, 141-155
  • MARKOWSKI, E.P., MARKOWSKI, C.A., 2002. “Improved attribute acceptance sampling plans in the presence of misclassification error”. European Journal of Operational Research, 139, 501-510
  • MURATA, T., ISHIBUCHI, H., TANAKA, H., 1996a. “Genetic Algorithms for Flow Shop Scheduling Problems”. Computers and Industrial Engineering vol.30, No.4, pp 1061-1071
  • MURATA, T., ISHIBUCHI, H., TANAKA, H., 1996b. “Multi-Objective Genetic Algorithms and Its Applications to Flow Shop Scheduling”. Computers and Industrial Engineering, vol 30, No 4, pp 957-968
  • PARKINSON, D.B. 1988. “Optimum Sampling Plans Based On Post- Quality Control Reliability”, Reliability Engineering and System Safety. 21, 59-75
  • PEARN, W. L., WU, C.W. 2005. “An Effective Decision Making Method for Product Acceptance”. Omega, Article in Press
  • RONEN, B., SPECTOR, Y. 1995. Evaluating Sampling Strategy Under Two Criteria, European Journal of Operational Research, 80, 59-67
  • SOHN, S.Y., JANG, J.S., 2001. Acceptance sampling based on reliability degradation data, Reliability Engineering and System Safety, 73, 67-72
  • TAGARAS, G., LEE, H.L., 1987. “Optimal Bayesian Single Sampling Attributes Plans with Modified Beta Prior Distribution”, Naval Research Logisitics, 34, 789-801
  • TAGARAS, G., 1994. Economic Acceptance Sampling By Variables With Quadratic Quality Costs, IIE Transactions, vol 26, no 6
  • WALL, M.S., ELSHENNAWY, A.K. 1989. “Economically Based Acceptance Sampling Plans”, Computers Industrial Engineering, 17,340-346
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Research Article
Yazarlar

İhsan Kaya Bu kişi benim

Yayımlanma Tarihi 1 Şubat 2006
Yayımlandığı Sayı Yıl 2006 Sayı: 15

Kaynak Göster

APA Kaya, İ. (2006). ÇOK AŞAMALI PROSESLERDE ÖRNEK HACMİNİN BELİRLENMESİÜZERİNE BİR MODEL VE GENETİK ALGORİTMALAR YARDIMIYLA ÇÖZÜM ÖNERİSİ. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(15), 435-456.


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