Çok Aşamalı Proseslerde Örnek Hacminin Belirlenmesi Üzerine Bir Model ve Genetik Algoritmalar Yardımıyla Çözüm Önerisi

Bu çalışmada, çok aşamalı kabul örneklemesi problemleri için Genetik Algoritma (GA) yaklaşımı incelenmiştir. Langner (2001) tarafından geliştirilen model kullanılarak, çok aşamalı muayene probleminin çözümüne ilişkin Visual Basic 6.0 programlama dilinde bir program hazırlanmış ve GA ile çözülen bu modelden elde edilen sonuçlar ANSI/ASQC Z1.4 örnekleme planı ile karşılaştırılmıştır. Her iki örnekleme planı için 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:

yöneylem araştırması

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.
Keywords:

operations research,

___

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