Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing

Doganis, P; Alexandridis, A; Patrinos, P; Sarimveis, H

dc.contributor.author	Doganis, P	en
dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Patrinos, P	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:25:24Z
dc.date.available	2014-03-01T01:25:24Z
dc.date.issued	2006	en
dc.identifier.issn	0260-8774	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17652
dc.subject	Dairy products	en
dc.subject	Evolutionary computation	en
dc.subject	Fresh milk	en
dc.subject	Genetic algorithms	en
dc.subject	Neural networks	en
dc.subject	Sales forecasting	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.classification	Food Science & Technology	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Neural networks	en
dc.subject.other	Sales	en
dc.subject.other	Time series analysis	en
dc.subject.other	Fresh milk	en
dc.subject.other	Product quality	en
dc.subject.other	Production planning	en
dc.subject.other	Dairy products	en
dc.title	Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jfoodeng.2005.03.056	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jfoodeng.2005.03.056	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	Due to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models, The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products. (c) 2005 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Journal of Food Engineering	en
dc.identifier.doi	10.1016/j.jfoodeng.2005.03.056	en
dc.identifier.isi	ISI:000236646500006	en
dc.identifier.volume	75	en
dc.identifier.issue	2	en
dc.identifier.spage	196	en
dc.identifier.epage	204	en