Yıl: 2001 Cilt: 2 Sayı: 1 Sayfa Aralığı: 137 - 157 Metin Dili: Türkçe İndeks Tarihi: 29-07-2022

Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması

Öz:
Rekabetin gittikçe şiddetlendiği günümüzün müşteri odaklı pazarlarında, ekonomik bir üretimi başarabilmek için geliştirilecek bir hücresel üretim sisteminin tasarımında, ilk adım tesisteki tezgahların hücre adı verilen daha küçük alt gruplara bölünmesi olmaktadır. Bu çalışmada, hücresel üretim sistemleri ve genetik algoritmalarla ilgili temel kavramlara kısaca değindikten sonra, çok ölçütlü karar ortamında tezgah-parça gruplandırması yapmak üzere tasarlanan bir Genetik Algoritma tanıtılıp, bunun son yıllarda literatürde kabul görmüş çözüm yollarıyla karşılaştırılması ve değerlendirilmesi yapılmaktadır.
Anahtar Kelime:

Forming manufacturing cells by using a genetic algorithm

Öz:
The first step to design a cellular manufacturing system is the decomposition of a set of machines of a manufacturing facility into smaller subsets, the so called cells, to achieve economic production to cope with ever increasing competition in today's customer oriented market. This study, after briefly introducing the basics of cellular manufacturing and Genetic Algorithms, presents a Genetic Algorithm designed to arrange machines as cells, in multi criteria decision making environment. Finally a comparison and an evaluation is made against the most recent accepted solution methods, found in technical literature.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Al-Sultan, K. S., Hussain, M. E, Nizami, J. S. (1996). A genetic algorithm for the set covering problem. Journal of the OR Society, 47, 702-709.
  • Adil, G. K., Rajamani, D. ve Strong, D. (1993). A mathematical model for cell formation considering investment and operational costs. European Journal of Operations Research, 69(3), 330-341.
  • Askin, R. G. ve Chiu, K. S. (1990). A graph partitioning procedure for machine assignment and cell formulation in GT. International Journal of Production Research, 28(8), 1555-1572.
  • Austin, S. (1989). Introduction of Genetic Algorithm. AI Expert, March, 49-53.
  • Beatty, C. A. (1992). Implementing advanced manufacturing technologies: rules of the road. Sloan Management Review, Summer, 49-60.
  • Boe, W. J. ve Cheng, C. H. (1991). Aclose neighbor algorithm for designing cellular manufacturing systems. International Journal of Production Research, 29(10), 2097-2116.
  • Boktor, F. (1991). A linear formulation of the machine-part cell formation problem. International Journal of Production Research, 29(2), 343-356.
  • Burbidge, J. L. (1992). Change to group technology: Process organization is obsolete. International Journal of Production Research, 30(5), 1209-1219.
  • Burges, A. G., Morgan, 1. ve Vollmann, T. E. (1993). Cellular manufacturing: It's impact on total factory. International Journal of Production Research, 31(9), 2059-2077.
  • Chan, K. C. ve Tansri, H. (1994). A study of genetic crossover operations on the facilities layout problem. Journal of Computers and Industrial Engineering, 26, 537-550.
  • Chen, C. L„ Cortruvo, N. A. ve Beak, W. (1995). A simulated annealing solution to the cell formation problem. International Journal of Production Research, 33, 2601-2614.
  • Choobineh, F. (1988). A framework for the design of cellular manufacturing systems. International Journal of Production Research, 26, 1161-1172.
  • Chow. W. S. ve Hawaleshka (1993). A novel machine grouping and know ledge-based approach for cellular manufacturing. European Journal of Operations Research. 69(3), 357-375.
  • Chu, C.H. (1993). Manufacturing cell formation by competitive learning. International Journal of Production Research, 31(4), 829-843.
  • Chu, C.H. ve Hayya, J. C. (1991). A fuzzy clustering approach to manufacturing cell formation. International Journal of Production Research, 29(7), 1475-1487.
  • Da Silveira, G. (1999). A methodology of implementation of cellular manufacturing, International Journal of Production Research, 37(2), 467-479.
  • Dengiz, B. ve Altıparmak, F. (1998). Genetik Algoritmalara Genel Bir Bakış. Endüstri Mühendisliği, 9(3)3-14.
  • Eckstein, A. L. H. ve Rohleder. T. R. (1998). Incorporating Human Resources in GT/CM. International Journal of Production Research, 30(5), 1199-1222.
  • Ferreira, J.F. ve Pradin, B. (1993). A methodology for cellular manufacturing design. International Journal of Production Research, 31(1), 235-250.
  • Gallagher, C. C. ve Knight, W. A. (1986). Group Technology Production Methods in Manufacture. John Wiley and Sons, Inc., New York.
  • Glover, F. ve Greenberg, H. J. (1989). New approaches for heuristic search: A bilateral linkage with artificial intelligence. European Journal of Operational Research, 39, 119-130.
  • Grefenstette, J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16, 122-128
  • Groover, M.P. (1987). Automation, Production Systems and CIM, Prentice Hall International Inc., New-Jersey.
  • Gupta, T. ve Seifoddini H. (1990). Production data based similarity coefficient for machine-component grouping decisions in design of cellular manufacturing systems. International Journal of Production Research, 28(7), 1247-1269.
  • Heragu, S. S. (1994). Group technology and cellular manufacturing. Transactions on Systems, Man and Cybernetics, 24(2), 203-215.
  • Jajodia, S., Minis, I., HarhaJakis, G. ve Proth, J. M. (1992). CLASS: Computerized layout solution using simulated annealing. International Journal of Production Research, 30(1), 95-108.
  • Jang, J. S. R., Sun, C. T. ve Mizutam, E. (1997). Neuro-Fuzzy and Soft Computing, Prentice Hall Inc., New Jersey.
  • Kamrani, A. K. ve Parsei, H. R. (1992). A methodology for forming manufacturing cells using manufacturing and design attributes. Computers and Industrial Engineering, 23(1/4), 73-76.
  • Kannan, V. R. (1996). Incorporating the impact of learning in assessing the effectiveness of cellular manufacturing. International Journal of Production Research, 34, 3327-3340.
  • Kaparthi, S., Suresh, N.C. ve Cerveny, R. P. (1993). An improved neural network leader algorithm for part-machine grouping in GT. European Journal of Operations Research, 69(3), 342-356.
  • Kaparthi, S, ve Suresh, N.C. (1990). Machine-component cell formation in GT: A neural network approach. International Journal of Production Research, 30(6), 1353-1367
  • Kar Yan Tam (1992). A simulated annealing algorithm for allocating space to manufacturing cells. International Journal of Production Research, 30(1), 63-87.
  • Kar Yan Tam, (1992a). A simulated annealing algorithm for allocating space to manufacturing cells. International Journal of Production Research, 30, 63-87.
  • Kotter, J. P. (1995). Leading change: Why transformation efforts fail. Harvard Business Review, March-April, 59-67.
  • Kumar, P., Tewari, N.K. ve Singh, N. (1990). Joint consideration of grouping and loading problems in a flexible manufacturing system. International Journal of Production Research, 28(7), 1345-1356.
  • Kusiak, A. ve Cho, M. (1992). Similarity coefficient algorithms for solving the group technology problem. International Journal of Production Research, 30(11), 2633-2646
  • Lee, H. ve Diaz, G. (1993). A network flow approach to solve clustering problems in GT. International Journal of Production Research, 31(3), 603-612.
  • Logendran, R. (1991). Impact of sequence of operations and layout of cells in cellular manufacturing. International Journal of Production Research, 29(2), 375-390.
  • Lozano, S., Guerrero, F., Eguia, I. ve Onieva, L. (1999). Cell Design and Loading in Presence of Alternative Routing. International Journal of Production Research, 37(14), 3289-3304.
  • Marsh, R. F., Shafer, S. M. ve Meredith, J. R. (1999). A Comparison of Cellular Manufacturing Research Presumptions with Practice. International Journal of Production Research, 37(14), 3119-3138.
  • Michalewicz, Z., (1994). Genetic Algorithms + Data Structures = Evolution Programs. Springer Ver-lag.
  • Michalewicz, Z., Dasgupta, D., Le Riche, R. G., and Schoenauer, M. (1996). Evolutionary algorithms for constrained engineering problems. Com puters and Industrial Engineering, 30, 851-870.
  • Morris, J.S. ve Tersine, R.J. (1990). A simulation analysis of factors influencing the attractiveness of group technology and cellular layouts. Management Science, 36(12), 1567-1578.
  • Purcheck, G. (1985). Machine-component group formation: An heuristic method for flexible produc tion cells and FMS. International Journal of Production Research, 23(5), 911-943.
  • Saaty, T. L., (1980). The Analytic Hierarchy Process, Mc Graw Hill.
  • Sarker, B. R. ve Mondal, S. (1999). Grouping Efficiency Measures of Cellular Manufacturing. International Journal of Production Research, 37(2), 285-314. Anadolu University Journal of Science and Technology, 2(1)
  • Seifoddini, H. ve Tjahjana, B. (1999), Part-family Formation for Cellular Manufacturing: A Case Study at Hamishfeger. International Journal of Production Research, 37(14), 3263-3273.
  • Seifoddini, H, (1990). Machine-component group analysis versus the similarity coefficient method in cellular manufacturing applications. Computers and Industrial Engineering, 18(3), 333-339.
  • Shafer, S.M. ve Rogers, D.F. (1993). Similarity and distance measures for cellular manufacturing Part I. International Journal of Production Research, 31(5), 1133-1142.
  • Shang, J. S. ve Tadihamalla. P. R. (1998). Multi-criteria design and control of a cellular manufacturing system trough simulation and optimization. International Journal of Production Research, 36(6), 1515-1528.
  • Singh, N. (1993). Design of cellular manufacturing systems: An invited review. European Journal of Operations Research, 69(3), 284-291.
  • Sirinaovakul, B. ve Thajchayapong, P. (1994). A knowledge base to assist a heuristic search approach to facility layout. International Journal of Production Research 1, 141-160.
  • Srinivas, M. ve Patnaik, L. M. (1994). Genetic Algorithms: A Survey. IEEE Computer, June, 17-26.
  • Sofianopoulou, S. (1997). Application of simulated annealing to a linear model for the formulation of machine cells in group technology. International Journal of Production Research, 35, 501-511.
  • Sofianopoulou, S. (1999). Manufacturing Cells design with Alternative Process Plans and/or Replicate Machines. International Journal of Production Research, 37(3), 707-720.
  • Spiliopulos, K. ve Sofianopoulou, S. (1996). An optimal tree search method for the manufacturing system cell formation problem. European Journal of Operational Research, 105,537-551.
  • Sule, D. R. (1991). Machine capacity planning in GT. International Journal of Production Research, 29(9), 1909-1922.
  • Sun, D., Kim, L. ve Batta, R. (1995). Celi formation using tabu search. Computers and Industrial Engineering, 28, 485-494.
  • Tate, D. M. ve Smith, A. E. (1995). A genetic approach to the quadratic assignment problem. Computers and Operations Research, 22, 73-84.
  • Venugopal, V. ve Narendran, T. T. (1992). A genetic algorithm approach to the machine-component grouping problem, Computers and Industrial Engineering, 22(4), 469-480:
  • Venugopal, V. ve Narendran, T.T. (1994). Machine cell formation through neural network models. International Journal of Production Research, 32(9), 2105-2116.
  • Venugopal, V. (1999). Soft-computing-based Approaches to Group Technology Problem: A State-of-the-art Review. International Journal of Production Research, 37(14), 3335-3357.
  • Warndecke, H.J. (1985). FMS Research viewpoint. Proceedings of the 4th International Conference on EMS, (Ed.) Lindholm R., 1-12. Stockholm, İsveç.
  • Wemmerlov, V. ve Ryer, N.L. (1989). Cellular manufacturing in US industry: A survey of users. International Journal of Production Research, 27(9), 1511-1530.
  • Winter, G., Perinax, J., Galau, M. ve Cuerta, P. (1995). Genetic Algorithms in Engineering and Computers Science, Wiley and Sons Inc., New Jersey.
APA İŞLİER A (2001). Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. , 137 - 157.
Chicago İŞLİER A. Attila Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. (2001): 137 - 157.
MLA İŞLİER A. Attila Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. , 2001, ss.137 - 157.
AMA İŞLİER A Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. . 2001; 137 - 157.
Vancouver İŞLİER A Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. . 2001; 137 - 157.
IEEE İŞLİER A "Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması." , ss.137 - 157, 2001.
ISNAD İŞLİER, A. Attila. "Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması". (2001), 137-157.
APA İŞLİER A (2001). Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, 2(1), 137 - 157.
Chicago İŞLİER A. Attila Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik 2, no.1 (2001): 137 - 157.
MLA İŞLİER A. Attila Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, vol.2, no.1, 2001, ss.137 - 157.
AMA İŞLİER A Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik. 2001; 2(1): 137 - 157.
Vancouver İŞLİER A Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik. 2001; 2(1): 137 - 157.
IEEE İŞLİER A "Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması." Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, 2, ss.137 - 157, 2001.
ISNAD İŞLİER, A. Attila. "Üretim hücrelerinin bir genetik algoritma kullanılarak oluşturulması". Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik 2/1 (2001), 137-157.