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

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.

Anahtar Kelimeler:

İşletme, yapay sinir ağları, yemek, talep, tahmin

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

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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