Tam Zamanlı Montaj Hatlarında Çok Amaçlı Karışık Model Sıralama İçin Rota Birleştirmeli Açgözlü Rassallaştırılmış Uyarlamalı Arama Yordamı

Bu calısma, bir Tam Zamanında Uretim (TZU) sisteminde hazırlık sayısının ve malzeme kullanım oranı dengesinin es zamanlı eniyilemesini amaclayan karısıkmodel montaj hattı sıralama problemi icin Acgozlu Rassallastırılmıs Uyarlamalı Arama Yordamı (GRASP) sezgiselinin yeni bir uygulamasını sunmaktadır. Birçok test problemi GRASP ile cozulmus ve sonuclar, tam sayımlama ve yasaklı arama, genetik algoritmalar, Kohonen self-organizing map sezgisellerinin literatürden alınan sonuclarıyla kıyaslanmıstır. Test sonucları, Rota birlestirmeli (Path relinking) GRASP’ı her iki amac cinsinden en iyiye yakı değrler urettiğni ve ortalama % basarıılı ve ortalama yuzdelik performansıı da diğr sezgisellerden daha ustun olduğnu gostermektedir. Bununla birlikte GRASP’ı islemci (CPU) suresi performansıgoreceli olarak zayı cımıtı.

Anahtar Kelimeler:

Cok amaclı karar verme, Karısık-model sıralama, Sezgiseller, Acgozlu Rassallastırılmıs Uyarlamalı Arama Yordamı, Optimizasyon

A Grasp With Path-Relinking For Mixed-Model Sequencing With Multiple Objectives On JIT Assembly Lines

This research presents a new application of Greedy Randomized Adaptive Search Procedure (GRASP) to address the production sequencing problem for mixed-model assembly line in a just-in-time (JIT) production system when two objectives are present: minimization of setups and optimization of stability of material usage rates. Several test problems are solved via GRASP and the results are compared to the solutions, taken from the literature, obtained via complete enumeration, tabu search, genetic algorithms and Kohonen self-organizing map approaches. Experimental results reveal that the GRASP with Path Relinking provides near-optimal solutions in terms of the two objectives and its “average inferiority %” and “average percentile” performances are superior to that of other heuristics. Results also show that the GRASP performs a little poorly with regard to CPU time.

Keywords:

Multiple objective decision making, Mixed-model sequencing, Heuristics, Greedy randomized adaptive search procedure, Optimization,

PDF

___

[1] S. Alpay, “Birden fazla montaj bandı ve ürün çesidi olan isletmelerde bilgisayar destekli üretim programı ve çizelgeleme sistemi tasarım”ı, Thesis (Master) (in Turkish), Eskisehir Osmangazi University, Turkey, 1997.
[2] J. Miltenburg, “Level schedules for mixed-model assembly lines in just-intime production systems”. Management Science, 35 (2), 192-207, 1989.
[3] P.R. McMullen, “JIT sequencing for mixed-model assembly lines with setups using tabu search”, Production Planning and Controlling, 9 (5), 504-510, 1998.
[4] P.R. McMullen, G.V. Fraizer, “A simulated annealing approach to mixedmodel sequencing with multiple objectives on a JIT line”, IIE Transactions, 32 (8), 679-686, 2000.
[5] P.R. McMullen, “An efficient frontier approach to addressing JIT sequencing problems with setups via search heuristics”, Computers & Industrial Engineering, 41, 335-353, 2001.
[6] P.R. McMullen, “A Kohonen self-organizing map approach to addressing a multiple objective , mixed-model JIT sequencing problem”, Int. J. Production Economics, 72, 59-71, 2001.
[7] P.R. McMullen, “An ant colony optimization approach to addressing a JIT sequencing problem with multiple objectives”, Artificial Intelligence in Engineering, 15, 309-317, 2001.
[8] P.R. McMullen, “A beam search heuristic method for mixed-model scheduling with setups”, Int. J. Production Economics, 96, 273-283, 2005.
[9] H.S. Cho, C.H. Paik, H.M. Yoon, H.G, Kim, “A robust design of simulated annealing approach for mixed model sequencing”, Computers & Industrial Engineering, 48, 753-764, 2005.
[10] S.A. Mansouri, “A multi-objective genetic algorithm for mixed-model sequencing on JIT assembly lines”, European Journal of Operational Research, 167, 696-716, 2005.
[11] R. Tavakkoli-moghadam, A.R. Rahimi-Vahed, “Multi-criteria sequencing problem for a mixed-model assembly line in a JIT production system”, Applied Mathematics and Computation, Article in Press, 2006.
[12] T.A. Feo J. Bard, “Flight scheduling and maintenance base planning”, Management Science, 35(12), 1415–1432, 1989.
[13] M.G.C. Resende, C.C. Riberio, Chapter 8: Greedy randomized adaptive search procedures, In: Glower F.W., Kochenberger G.A., (Eds.), Handbook of Metaheuristics, International Series in Operations Research and Management Science, Vol. 57, Kluwer Academic Publishers, 2002
[14] S.R. Gupta, J. Smith, “Algorithms for single machine total tardiness scheduling with sequence dependent setups”, European Journal of Operational Research, 175, 722-739, 2006
[15] R.M. Aiex, S. Binato, M.G.C. Resende, “Parallel GRASP with path-relinking for job shop scheduling”, Parallel Computing, 29, 393-430, 2003.