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

Year 2008, Volume: 21 Issue: 1, 153 - 170, 30.06.2008

Şerafettin Alpay

Abstract

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.

Keywords

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

Abstract

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

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