TY - JOUR T1 - İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi TT - An Estimation of Personnel Food Demand Quantity for Businesses by Using Artificial Neural Networks AU - Calp, M. Hanefi PY - 2019 DA - September DO - 10.2339/politeknik.444380 JF - Politeknik Dergisi PB - Gazi University WT - DergiPark SN - 2147-9429 SP - 675 EP - 686 VL - 22 IS - 3 LA - tr AB - Günümüzde kamu veya özel kurumlarınbirçoğu, bünyelerinde çalışan personeller için profesyonel yemek hizmetivermektedir. Söz konusu hizmetin planlanması konusunda, kurumlarda çalışanpersonel sayısının genel olarak fazla olması ve personellerin şahsi veya kurumaait sebeplerle kurum dışında olmalarından dolayı birtakım aksamalaryaşanmaktadır. Bu yüzden, günlük yemek talebinin belirlenmesi zorlaşmakta ve budurum kurumlar için maliyet, zaman ve emek kaybına sebep olmaktadır. Bukayıpları ortadan kaldırmak veya en azından minimuma indirmek amacıylaistatistiksel veya sezgisel yöntemler kullanılmaktadır. Bu çalışmada,işletmeler için yapay sinir ağları kullanılarak günlük yemek talebini tahmineden yapay zekâ tabanlı bir model önerilmiştir. Veriler, günlük yemek çıkaranve farklı kademlerde görev alan 110 kişilik bir personel kapasitesine sahipözel bir işletmenin yemekhane veritabanından elde edilmiş olup son 2 yıllık(2016-2018) veriyi kapsamaktadır. Model, MATLAB paket programı kullanılarakoluşturulmuştur. Modelin performansı, Regresyon değerleri, Ortalama Mutlak HataYüzdesi (OMHY-MAPE) ve Ortalama Karesel Hata (OKH-MSE) dikkate alınarakbelirlenmiştir. Ağın eğitiminde, ileri beslemeli geri yayılımlı ağ mimarisikullanılmıştır. Denemeler sonucunda elde edilen en iyi model, sırasıyla eğitimR oranı: 0,9948, test R oranı: 0,9830 ve hata oranı ise 0,003783 olup çokkatmanlı (8-10-10-1) bir yapıya sahiptir. Deney sonuçları, modelin hataoranının düşük, performansının yüksek olduğunu ve talep tahmini için yapaysinir ağları kullanımının olumlu etkisini ortaya koymuştur. KW - İşletme KW - yapay sinir ağları KW - yemek KW - talep KW - tahmin N2 - Today, manypublic or private institutions provide professional food service for personnelsworking in their own organizations. Regarding the planning of the said service,there are some obstacles due to the fact that the number of the personnelworking in the institutions is generally high and the personnel are out of theinstitution due to personal or institutional reasons. Because of this, it isdifficult to determine the daily food demand, and this causes cost, time andlabor loss for the institutions. Statistical or heuristic methods are used toremove or at least minimize these losses. In this study, an artificialintelligence model was proposed, which estimates the daily food demand quantityusing artificial neural networks for businesses. The data are obtained from arefectory database of a private institution with a capacity of 110 peopleserving daily meals and serving at different levels, covering the last twoyears (2016-2018). The model was created using the MATLAB package program. Theperformance of the model was determinde by the Regression values,  the Mean Absolute Percentage Error (MAPE) andthe Mean Squared Error (MSE). In the training of the ANN model, feed forwardback propagation network architecture is used. The best model obtained as a resultof the experiments is a multi-layer (8-10-10-1) structure with a training Rratio 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 errorrate, high performance and positive effect of using artificial neural networksfor demand estimating. 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