Modelling Approaches to Food Waste: Discrete event simulation; machine learning; Bayesian networks; agent-based modelling; and mass balance estimation

Cansu Kandemir, Christian Reynolds, Monika Verma, Matthew Grainger, Gavin Stewart, Simone Righi, Simone Piras, Marco Setti, Matteo Vittuari, Tom Quested

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The generation of food waste at both the supplier and the consumer levels stems from a complex set of interacting behaviours. Computational and mathematical models provide various methods to simulate, diagnose and predict different aspects within the complex system of food waste generation and prevention. This chapter outlines four different modelling approaches that have been used previously to investigate food waste: discrete event simulation, which has been used to examine how the shelf life of milk and many actions taken around shopping and use of milk within a household influence food waste; machine learning and Bayesian networks, which have been used to provide insight into the determinants of household food waste; agent-based modelling, which has been used to provide insight into how innovation can reduce retail food waste; and mass balance estimation, which has been used to model and estimate food waste from data related to human metabolism and calories consumed.
Original languageEnglish
Title of host publicationRoutledge Handbook of Food Waste
EditorsChristian Reynolds, Tammara Soma, Charlotte Spring, Jordon Lazell
Place of PublicationLondon
PublisherRoutledge
Pages326-344
ISBN (Electronic)9780429462795
ISBN (Print)9781138615861
DOIs
Publication statusPublished - 31 Jan 2020

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