A comparison of the performance of artificial intelligence techniques for optimizing the number of kanbans

C Alabas, F Altiparmak () and B Dengiz ()
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C Alabas: Gazi University, Maltepe
F Altiparmak: Gazi University, Maltepe
B Dengiz: Gazi University, Maltepe

Journal of the Operational Research Society, 2002, vol. 53, issue 8, 907-914

Abstract: Abstract This paper discusses the use of modern heuristic techniques coupled with a simulation model of a Just in Time system to find the optimum number of kanbans while minimizing cost. Three simulation search heuristic procedures based on Genetic Algorithms, Simulated Annealing, and Tabu Search are developed and compared both with respect to the best results achieved by each algorithm in a limited time span and their speed of convergence to the results. In addition, a Neural Network metamodel is developed and compared with the heuristic procedures according to the best results. The results indicate that Tabu Search performs better than the other heuristics and Neural Network metamodel in terms of computational effort.

Keywords: kanban; Just in Time system; kanban-controlled system; simulation optimization; genetic algorithms; simulated annealing; tabu search; neural network (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (4)

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DOI: 10.1057/palgrave.jors.2601395

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