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Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty by Mohammadali Faezirad; Alireza Pooya; Zahra Naji-Azimi; Maryam Amir Haeri is a Environmental Science article available to read on EtoBox.

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Food waste planning at universities is often a complex matter due to the large volume of food and variety of services. A major portion of university food waste arises from dining systems including meal booking and distribution. Although dining systems have a significant role in generating food wastes, few studies have designed prediction models that could control such wastes based on reservation data and behavior of students at meal delivery times. To fill this gap, analyzing meal booking systems at universities, the present study proposed a new model based on machine learning to reduce the food waste generated at major universities that provide food subsidies. Students’ reservation and their presence or absence at the dining hall (show/no-show rate) at mealtime were incorporated in data analysis. Given the complexity of the relationship between the attributes and the uncertainty observed in user behavior, a model was designed to analyze definite and random components of demand. An artificial neural network-based model designed for demand prediction provided a two-step prediction approach to dealing with uncertainty in actual demand. In order to estimate the lowest total cost based

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Author

Mohammadali Faezirad; Alireza Pooya; Zahra Naji-Azimi; Maryam Amir Haeri

Publisher

SAGE Publications

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)