ÇOK AMAÇLI TESİS YERLEŞİM PROBLEMİ İÇİN YENİ BİR MELEZ SEZGİSEL ALGORİTMA

Yıl 2010, Cilt: 25 Sayı: 1, 0 - , 15.02.2013

Ramazan Şahin , Orhan Türkbey

Öz

Bu makalede, Çok Amaçlı Tesis Yerleşim Probleminin (ÇATYP) çözümü için, tabu listesi ile desteklenmiş,Tavlama Benzetimi’ne (TB) dayalı yeni bir melez sezgisel algoritma önerilmektedir. TB’ne tabu listesinineklenmesiyle oluşturulan yeni melez sezgisel algoritmanın en önemli avantajı, TB’nin olasılıklı yapısı korunurken,aynı zamanda kısa dönemli hafıza (tabu listesi) ile daha önce üretilen komşu çözümlerin tekrar üretilmesininengellenmesidir. Yeni melez sezgisel algoritmanın amacı, ÇATYP’nin etkin çözümler kümesini (pareto optimalset) oluşturan çözümleri kısa zaman içinde bulmaktır. Yeni melez sezgisel algoritmanın etkinliği 8 bölüm ve 4amaçtan oluşan bir örnek problem üzerinde gösterilmiştir. Algoritma ile önceden belirlenmiş ağırlıklar için en iyiyerleşim planı ile aynı zamanda problemin etkin çözümlerinin oluşturduğu pareto çözümler kümesi bulunmuştur.

Anahtar Kelimeler

Tesis yerleşim problemi, çok amaçlı eniyileme, pareto optimalite, melez sezgisel algoritma.

Kaynakça

• Jajodia, S., Minis, I., Harhalakis, G. and Proth,
• J.M., “CLASS: computerized layout solutions
• using simulated annealing”, International
• Journal of Production Research, Cilt 30, No 1,
• –108, 1992.
• Muther, R., Systematic Layout Planning (SLP),
• nd Edition, Cahners Books, Boston, 1974.
• Armour, G.C. and Buffa, E.S., “A heuristic
• algorithm and simulation approach to relative
• allocation of facilities”, Management Science,
• Cilt 9, 294–309, 1963.
• Houshyar, A., “Computer aided facilities layout:
• an interactive multi-goal approach”, Computers
• and Industrial Engineering, Cilt 20, No 2, 177-
• , 1991.
• Chen, C.-W. and Sha, D.Y., “Heuristic approach
• for solving the multi-objective facility layout
• problem”, International Journal of Production
• Research, Cilt 43, No 21, 4493-4507, 2005.
• Malakooti, B., “Multiple objective facility layout:
• a heuristic to generate efficient alternatives”,
• International Journal of Production Research,
• Cilt 27, No 7, 1225–1238, 1989.
• Sha, D.Y. and Chen, C.-W., “A new approach to
• the multiple objective facility layout problem”,
• Integrated Manufacturing Systems, Cilt 12, No
• , 59-66, 2001.
• Rosenblatt, M.J., “The facilities layout problem:
• a multi-goal approach”, International Journal
• of Production Research, Cilt 17, No 4, 323-332,
• -
• Dutta, K.N. and Sahu, S., “A multigoal heuristic
• for facilities design problems: MUGHAL”,
• International Journal of Production Research,
• Cilt 20, No 2, 147–154, 1982.
• Fortenberry, J.C. and Cox, J.F., “Multiple criteria
• approach to the facilities layout problem”,
• International Journal of Production Research,
• Cilt 23, No 4, 773–782, 1985.
• Urban, T.L., “A multiple criteria model for the
• facilities layout problem”, International Journal
• of Production Research, Cilt 25, No 12, 1805–
• , 1987.
• Harmonosky, C.M. and Tothero, G.K., “A multifactor
• plant layout methodology”, International
• Journal of Production Research, Cilt 30, No 8,
• –1789, 1992.
• Suresh, G. and Sahu, S., “Multiobjective facility
• layout using simulated annealing”, International
• Journal of Production Economics, Cilt 32, No
• , 239-254, 1993.
• Chen, C.-W. and Sha, D.Y., “A design approach
• to the multi-objective facility layout problem”,
• International Journal of Production Research,
• Cilt 37, No 5, 1175–1196, 1999.
• Hahn, P.M. and Krarup, J., “A hospital facility
• layout problem finally solved”, Cilt 12, No 5-6,
• -496, 2001.
• Kirkpatrick, S., Gelatt, Jr.C.D. and Vecchi, M.P.,
• “Optimization by Simulated Annealing”,
• Science, Cilt 220, No 4598, 671-680, 1983.
• Blum, C. and roli, A., “Metaheuristics in
• combinatorial optimization: Overview and
• conceptual comparison”, ACM Computing
• Surveys, Cilt 35, No 3, 268-308, 2003.
• Elmohamed, S., Fox, G. and Coddington, P., “A
• Comparison of Annealing Techniques for
• Academic Course Scheduling”, In proceedings
• of the 2nd International Conference on the
• Practice and Theory of Automated
• Timetabling, Syracuse, NY, USA, April 4, 146-
• , 1998.
• Zhang, L. and Wang L., “Optimal parameters
• selection for simulated annealing with limited
• computational effort”, IEEE International
• Conference on Neural Networks and Signal
• Processing, Nanjing, China, December 14-17,
• -415, 2003.
• Glover, F., “Heuristics for Integer Programming
• Using Surrogate Constraints”, Decision Science,
• Cilt 8, No 1, 156-166, 1977.
• Zolfaghari, S. and Liang, M., “Jointly solving the
• group scheduling and machining speed selection
• problems: A hybrid tabu simulated and simulated
• annealing approach”, International Journal of
• Production Research, Cilt 37, No 10, 2377–
• , 1999.
• Swarnkar, R. and Tiwari, M.K., “Modeling
• machine loading problem of FMSs and its
• solution methodology using a hybrid tabu search
• and simulated annealing-based heuristic
• approach”, Robotics and Computer-Integrated
• Manufacturing, Cilt 20, No 3, 199–209, 2004.
• Zhang, D. and Deng, A., “An effective hybrid
• algorithm for the problem of packing circles into
• a larger containing circle”, Computers and
• Operations Research, Cilt 32, 1941–1951, 2005.
• Şahin, R. and Türkbey, O., “A new hybrid tabusimulated
• annealing heuristic for the dynamic
• facility layout problem”, International Journal
• of Production Research, Cilt 47, No 24, 6855–
• , 2009.
• Wattanapongsakorn, N. and Levitan, S.P.,
• “Reliability optimization models for embedded
• systems with multiple applications”, IEEE
• Transactions on Reliability, Cilt 53, No 3, 406–
• , 2004.
• Deb, K., “Multi-Objective Optimization using
• evolutionary Algorithms”, 1st Edition, Wiley,
• New York, 2001.