EMEK YOĞUN ÇALIŞILAN PROJE TİPİ ÜRETİM SİSTEMLERİ İÇİN ARTIK TERİM TABANLI AKIŞ ZAMANI TAHMİN ALGORİTMASI

Yıl 2016, Cilt: 18 Sayı: 54, 580 - 595, 01.09.2016

Aslan Deniz Karaoglan

Öz

Regression-based methods are widely used for flow time estimation of customer orders. However, for the customer orders that will be produced for the first time in a labor intensive project type production system with new design parameters, it is hard to make thoroughly accurate flow time prediction at the quotation stage. This is caused by having so many uncontrollable factors in a production system, that are not placed in the mathematical models. These uncontrollable factors cause high differences between the observed and expected flow time. In this study, a new algorithm - that combines the regression analysis and the artificial neural networks - is proposed. By this way, the prediction performance of fitted regression model is improved and the lack-of-fit is decreased

Anahtar Kelimeler

Regression analysis, neural network applications, flow time, project type production

RESIDUAL BASED FLOW TIME ESTIMATION ALGORITHM FOR LABOR INTENSIVE PROJECT TYPE PRODUCTION SYSTEMS

Öz

Regresyon tabanlı metodlar, müşteri siparişlerinin akış zamanının hesaplanmasında yaygın olarak kullanılmaktadır. Ancak emek yoğun proje tipi üretim yapan işletmelerde, ilk defa yeni tasarım parametreleri ile üretilecek olan ürünlerin akış zamanını, üretime başlamadan önce müşteriye fiyat teklifi verme aşamasında tahmin etmek zor bir problemdir. Bu durum, üretim sisteminin matematiksel modellerde yer verilemeyen pek çok kontrol edilemeyen değişken içermesinden kaynaklanır. Bu kontrol edilemeyen değişkenler ise beklenen akış zamanı ile regresyon tabanlı denklemlerle tahmin edilen zamanlar arasında hatırı sayılır bir tahmin hatasını ortaya çıkarır. Bu çalışmada, regresyon denkleminin tahmin hatasını minimize etmek üzere, yapay sinir ağları ile regresyon analizini birleştiren bir algoritma önerilmiştir. Bu yolla regresyon denkleminin tahmin performansı arttırılmış ve tahmin hataları minimize edilmiştir

Anahtar Kelimeler

regresyon analizi, yapay sinir ağları, akış zamanı, proje tipi üretim

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