Prediction of Cold Chain Transport Conditions Using Data Mining

Autor:	Clayton Gerber Mangini, Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs
Rok vydání:	2020
Předmět:	Data set Ensure (product) Naive Bayes classifier Reducer business.industry Multilayer perceptron Added value Environmental science Cold chain Process engineering business
Zdroj:	IFIP Advances in Information and Communication Technology ISBN: 9783030579968 APMS (2)
Popis:	Ensuring the delivery of temperature-controlled products in transportation is an increasing challenge, especially in countries with continental extension and tropical climate such as Brazil. Products with this type of specificity generally have a higher added value and involve specialized equipment and labor. Thus, route mapping is necessary for the logistics of the cold chain. The study aimed to predict the transport conditions in the cold chain. The data set analyzed includes the temperature of the loads and the route information (Southeast to Northeast and South of Brazil). The classification of temperature excursions considered data below 15 °C or above 30 °C. The Naive Bayes and Multilayer Perceptron algorithms are used to predict the optimal temperature excursion model. The Multilayer Perceptron algorithm proved to be the most suitable for a thermal route mapping model. With this identified standard, logistics decision making can be improved to reducer o waste and ensure product integrity with less recourse.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::72ab5dd8347ccc4b9625e4eee1d7d7af https://doi.org/10.1007/978-3-030-57997-5_71 Zobrazit plný text záznamu