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Yıl 2008, Cilt: 22 Sayı: 2, 357 - 377, 27.11.2010

Güzin Özdağoğlu

Öz

90’lardan bu yana geniş bir uygulama alanı bulan modern sezgisel
teknikler, bir problem çözümünde, kendi yerel arama sistemleri ile en iyiye en
yakın sonuca ulaşmayı amaçlamaktadırlar. Bu çözümler her zaman için tam
optimum sonucu bulmayı garanti edemezler, ancak bir çok olay ya da problem
için yeterli ve kaydadeğer olurlu çözüm bulabilirler. Bu tekniklerden bir de
tavlama benzetimi algoritmasıdır ve çalışmanın uygulama yöntemi olarak
seçilmiştir. Bu çalışmada seri çalışma prensibine sahip bir üretim akış hattı alt
süreci için sıralama probleminin çözümünde tavlama benzetimi algoritmasının
nasıl kullanıldığı sunulmakta ve örnek olay üzerinde uygulama anlatılmaktadır.
Tavlama benzetimi algoritmasından elde edilen sonuçlar tanınmış bir
çizelgeleme programı olan LEKIN’in sonuçlarıyla karşılaştırılmaktadır. Sonuç
olarak, sunulan SA algoritması ile gözetilen amaç doğrultusunda, çizelgeleme
uygulamalarında yaygın kullanıma sahip olan LEKIN programına yakın
sonuçlar elde edilmektedir.

Anahtar Kelimeler

tavlama benzetimi sıralama akış tipi çizelgeleme LEKIN

Kaynakça

  • Ahmed, A.M, Alkhamis, T.M.,(2002). Simulation Based Optimization using Simulated Annealing with Ranking and Selection. Computers and Operations Research, 29, p.387-402.
  • Aldowaisan, T.; Allahverdi, A. (2003). New heuristics for no-wait flow shops to minimize make span, Computers & Operations Research, 30, p. 1219–1231.
  • Allahverdi, A.; Aldowaisan, T. (2004). No-Wait Flow shops With Bicriteria of Make span And Maximum Lateness, European Journal of Operational Research, 152, p. 132–147.
  • Baykasoglu,A, Gyndy, N, (2001). A Simulated Annealing Algorithm For Dynamic Layout Problem, Computers & Operations Research, 28, p.1403-1426.
  • Bennage WA, Dhingra AK.(1995). Single And Multi-objective Structural Optimization in Discrete-Continuous Variables Using Simulated Annealing. International Journal for Numerical Methods in Engineering, 38, p. 2753-73.
  • Bounds, D. G. (1987). New Optimization Methods from Physics and Biology, Nature, 329, p. 215-218.
  • Brandimarte, P.(1995). Advance Models for manufacturing Systems Management. Florida:CRC Press
  • Burdett, R.L.; Kozan, E. (2000). Evolutionary algorithms for flow shop sequencing with non-unique jobs, International Transactions in Operations Research, 7, p.401-418.
  • Dorigo, M.; Gambardella, L.M.(1997) Ant Colonies for the Travelling Saleman Problem, Biosystems, 3 (2), p. (73-81).
  • Glover F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 1(3), p.533–49.
  • Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass.
  • Held, M.; Karp, R.M. (1970) The Traveling Salesman Problem and Minimum Spanning Trees.
  • Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
  • Hopfield, J.J.(1982). Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Science, USA, 79, p. 2554.
  • http://csep1.phy.ornl.gov/CSEP,25.12.2003
  • Katayama, K, Narihisa, H.(2001). Performance of Simulated Annealing –Based Heuristic for the Unconstraint Binary Quadratic Programming Problem. European Journal of Operations Research, 134, p.103-119.
  • Kirkpatrick, S., Gerlatt, C. D. Jr., and Vecchi, M.P.(1983). Optimization by Simulated Annealing, Science, 220, p.. 671-680.
  • Kubotani, H., Yoshimura, K.(2003). Performance Evaluation of Probability Functions for Multi-Objective Simulated Annealing. Computers and Operations Research, 30, p. 427-442.
  • Laporte G, Osman I. (1996). Metaheuristics: A bibliography, Annals of Operations Research, 63, p.513-623.
  • Lejeune, A.M.,(2003). Heuristic Optimization of Experimental Designs. European Journal of Operations Research, 147, p. 484-498.
  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M. N., Teller, A.H. and Teller, E.(1958) Equations of State Calculations by Fast Computing Machines, J. Chem. Phys. 21, p. 1087- 1092.
  • Pincus, M.(1970), A Monte Carlo Method for the Approximate Solution of Certain Types of Constrained Optimization Problems, Operations Research, 18, p. 1225-1228.
  • Pinedo, M. (1997). Scheduling Theory, Algorithms and Systems, New Jersey, Prentice-hall.
  • Randelman, R. E., and Grest, G.S.. (1997) N-City Traveling Salesman Problem - Optimization by Simulated Annealing, J. Stat. Phys. 45, p. 885-890.
  • Reeves, C.R (1993). Modern Heuristic Techniques for Combinatorial Problems (Blackwell, Oxford)
  • Singh,N, Ramajani D.,(1996). Cellular Manufacturing Systems, Design, Planning and Control,1st edition,Chapman and Hall.
  • T’kindt, V.; Monmarch, N.; Tercinet, F.; Lagugt, D.(2002). An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flow shop scheduling problem, European Journal of Operational Research, 142, p.250–257.
  • Varadharajan, T.K.; Rajendran, C., (2005). A multi-objective simulated- annealing algorithm for scheduling in flow shops to minimize the make span and total flow time of jobs, European Journal of Operational Research, 167, p. 772–795.
  • Wolfram, S.(1984) Universality and Complexity in Cellular Automata, Physica D: Nonlinear Phenomena, 10(1-2),p. 1-35.

Yıl 2008, Cilt: 22 Sayı: 2, 357 - 377, 27.11.2010

Güzin Özdağoğlu

Öz

Kaynakça

  • Ahmed, A.M, Alkhamis, T.M.,(2002). Simulation Based Optimization using Simulated Annealing with Ranking and Selection. Computers and Operations Research, 29, p.387-402.
  • Aldowaisan, T.; Allahverdi, A. (2003). New heuristics for no-wait flow shops to minimize make span, Computers & Operations Research, 30, p. 1219–1231.
  • Allahverdi, A.; Aldowaisan, T. (2004). No-Wait Flow shops With Bicriteria of Make span And Maximum Lateness, European Journal of Operational Research, 152, p. 132–147.
  • Baykasoglu,A, Gyndy, N, (2001). A Simulated Annealing Algorithm For Dynamic Layout Problem, Computers & Operations Research, 28, p.1403-1426.
  • Bennage WA, Dhingra AK.(1995). Single And Multi-objective Structural Optimization in Discrete-Continuous Variables Using Simulated Annealing. International Journal for Numerical Methods in Engineering, 38, p. 2753-73.
  • Bounds, D. G. (1987). New Optimization Methods from Physics and Biology, Nature, 329, p. 215-218.
  • Brandimarte, P.(1995). Advance Models for manufacturing Systems Management. Florida:CRC Press
  • Burdett, R.L.; Kozan, E. (2000). Evolutionary algorithms for flow shop sequencing with non-unique jobs, International Transactions in Operations Research, 7, p.401-418.
  • Dorigo, M.; Gambardella, L.M.(1997) Ant Colonies for the Travelling Saleman Problem, Biosystems, 3 (2), p. (73-81).
  • Glover F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 1(3), p.533–49.
  • Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, Mass.
  • Held, M.; Karp, R.M. (1970) The Traveling Salesman Problem and Minimum Spanning Trees.
  • Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.
  • Hopfield, J.J.(1982). Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Science, USA, 79, p. 2554.
  • http://csep1.phy.ornl.gov/CSEP,25.12.2003
  • Katayama, K, Narihisa, H.(2001). Performance of Simulated Annealing –Based Heuristic for the Unconstraint Binary Quadratic Programming Problem. European Journal of Operations Research, 134, p.103-119.
  • Kirkpatrick, S., Gerlatt, C. D. Jr., and Vecchi, M.P.(1983). Optimization by Simulated Annealing, Science, 220, p.. 671-680.
  • Kubotani, H., Yoshimura, K.(2003). Performance Evaluation of Probability Functions for Multi-Objective Simulated Annealing. Computers and Operations Research, 30, p. 427-442.
  • Laporte G, Osman I. (1996). Metaheuristics: A bibliography, Annals of Operations Research, 63, p.513-623.
  • Lejeune, A.M.,(2003). Heuristic Optimization of Experimental Designs. European Journal of Operations Research, 147, p. 484-498.
  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M. N., Teller, A.H. and Teller, E.(1958) Equations of State Calculations by Fast Computing Machines, J. Chem. Phys. 21, p. 1087- 1092.
  • Pincus, M.(1970), A Monte Carlo Method for the Approximate Solution of Certain Types of Constrained Optimization Problems, Operations Research, 18, p. 1225-1228.
  • Pinedo, M. (1997). Scheduling Theory, Algorithms and Systems, New Jersey, Prentice-hall.
  • Randelman, R. E., and Grest, G.S.. (1997) N-City Traveling Salesman Problem - Optimization by Simulated Annealing, J. Stat. Phys. 45, p. 885-890.
  • Reeves, C.R (1993). Modern Heuristic Techniques for Combinatorial Problems (Blackwell, Oxford)
  • Singh,N, Ramajani D.,(1996). Cellular Manufacturing Systems, Design, Planning and Control,1st edition,Chapman and Hall.
  • T’kindt, V.; Monmarch, N.; Tercinet, F.; Lagugt, D.(2002). An Ant Colony Optimization algorithm to solve a 2-machine bicriteria flow shop scheduling problem, European Journal of Operational Research, 142, p.250–257.
  • Varadharajan, T.K.; Rajendran, C., (2005). A multi-objective simulated- annealing algorithm for scheduling in flow shops to minimize the make span and total flow time of jobs, European Journal of Operational Research, 167, p. 772–795.
  • Wolfram, S.(1984) Universality and Complexity in Cellular Automata, Physica D: Nonlinear Phenomena, 10(1-2),p. 1-35.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil tr;en
Bölüm Makaleler
Yazarlar

Güzin Özdağoğlu Bu kişi benim

Yayımlanma Tarihi 27 Kasım 2010
Yayımlandığı Sayı Yıl 2008 Cilt: 22 Sayı: 2

Kaynak Göster

APA Özdağoğlu, G. (2010). Atatürk Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 22(2), 357-377.
 Kapak Resmi İndir

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