İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi

Year 2019, , 675 - 686, 01.09.2019

M. Hanefi Calp

https://doi.org/10.2339/politeknik.444380

Cited By: 12

Abstract

Günümüzde kamu veya özel kurumların
birçoğu, bünyelerinde çalışan personeller için profesyonel yemek hizmeti
vermektedir. Söz konusu hizmetin planlanması konusunda, kurumlarda çalışan
personel sayısının genel olarak fazla olması ve personellerin şahsi veya kuruma
ait sebeplerle kurum dışında olmalarından dolayı birtakım aksamalar
yaşanmaktadır. Bu yüzden, günlük yemek talebinin belirlenmesi zorlaşmakta ve bu
durum kurumlar için maliyet, zaman ve emek kaybına sebep olmaktadır. Bu
kayıpları ortadan kaldırmak veya en azından minimuma indirmek amacıyla
istatistiksel veya sezgisel yöntemler kullanılmaktadır. Bu çalışmada,
işletmeler için yapay sinir ağları kullanılarak günlük yemek talebini tahmin
eden yapay zekâ tabanlı bir model önerilmiştir. Veriler, günlük yemek çıkaran
ve farklı kademlerde görev alan 110 kişilik bir personel kapasitesine sahip
özel bir işletmenin yemekhane veritabanından elde edilmiş olup son 2 yıllık
(2016-2018) veriyi kapsamaktadır. Model, MATLAB paket programı kullanılarak
oluşturulmuştur. Modelin performansı, Regresyon değerleri, Ortalama Mutlak Hata
Yüzdesi (OMHY-MAPE) ve Ortalama Karesel Hata (OKH-MSE) dikkate alınarak
belirlenmiştir. Ağın eğitiminde, ileri beslemeli geri yayılımlı ağ mimarisi
kullanılmıştır. Denemeler sonucunda elde edilen en iyi model, sırasıyla eğitim
R oranı: 0,9948, test R oranı: 0,9830 ve hata oranı ise 0,003783 olup çok
katmanlı (8-10-10-1) bir yapıya sahiptir. Deney sonuçları, modelin hata
oranının düşük, performansının yüksek olduğunu ve talep tahmini için yapay
sinir ağları kullanımının olumlu etkisini ortaya koymuştur.

Keywords

İşletme, yapay sinir ağları, yemek, talep, tahmin

An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks

Abstract

Today, many
public or private institutions provide professional food service for personnels
working in their own organizations. Regarding the planning of the said service,
there are some obstacles due to the fact that the number of the personnel
working in the institutions is generally high and the personnel are out of the
institution due to personal or institutional reasons. Because of this, it is
difficult to determine the daily food demand, and this causes cost, time and
labor loss for the institutions. Statistical or heuristic methods are used to
remove or at least minimize these losses. In this study, an artificial
intelligence model was proposed, which estimates the daily food demand quantity
using artificial neural networks for businesses. The data are obtained from a
refectory database of a private institution with a capacity of 110 people
serving daily meals and serving at different levels, covering the last two
years (2016-2018). The model was created using the MATLAB package program. The
performance of the model was determinde by the Regression values,  the Mean Absolute Percentage Error (MAPE) and
the Mean Squared Error (MSE). In the training of the ANN model, feed forward
back propagation network architecture is used. The best model obtained as a result
of the experiments is a multi-layer (8-10-10-1) structure with a training R
ratio of 0,9948, a testing R ratio of 0,9830 and an error rate of 0,003783,
respectively. Experimental results demonstrated that the model has low error
rate, high performance and positive effect of using artificial neural networks
for demand estimating.

Keywords

Business, artificial neural networks, food, demand, estimation

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