Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty

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

doi:10.1177/0734242x211017974

Cited by 11 publications

(9 citation statements)

References 36 publications

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“…Faezirad et al ( 2021 ) noticed that the reduction of FW at one of the major universities in Tehran, Iran was based on decreasing the number of unclaimed meals, and to implement this approach, a simultaneous balance between minimizing waste cost and shortage penalty was considered. Besides, the authors proposed an artificial neural network-based model designed for demand prediction by a students’ reservation system that supports meal planning.…”

Section: Resultsmentioning

confidence: 99%

Toward food waste reduction at universities

Filho

Ribeiro

Setti

et al. 2023

Environ Dev Sustain

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Food waste is a serious problem, which undermines the achievement of many sustainable development goals (SDGs), despite their consideration in the agendas of many countries and companies. Notoriously, food waste (FW) causes different kinds of pollution that affect public health and social justice, while contributing to economic losses. This waste phenomenon has causes, drivers, and impacts that require rigorous assessments and effective approaches to mitigate its noxious effects, which are a serious concern for universities. Within these institutions, reducing food waste becomes a circular economy strategy, which is being utilized to assist in promoting sustainable development. However, there is a need for urgent attention to the specific causes of food waste and for consistent actions to reduce it, while boosting awareness in the campus community and triggering a change in students’ eating habits. The purpose of this study is to analyze what can be done to reduce the levels of food waste at universities. To achieve this, a review of the theme’s state of the art, which is inclusive of an overview of food waste production at universities around the world, is presented. The study employed a qualitative methodology where a comprehensive review of the literature and case studies analyses from selected world regions were considered. The data indicate that a broad variance exists in producing food waste among universities, from 0.12 to 50 kg/capita/day. More factors influence the problem (e.g., gender, age, season, consumer behavior), as well as strategies to solve and prevent it (e.g., composting, recycling, new designs of packages, trayless meals, education), and benefits leading toward food waste reductions from 13 to 50%. Also, four priority actions were identified to reduce food waste at universities, and these consist of planning and awareness, food preparation and storage, services, and direct waste reuse. With appropriate adaptations, these recommended actions should be deployed as means for reducing food waste at universities around the world, while expanding learning and education in sustainability.

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Section: Resultsmentioning

confidence: 99%

Toward food waste reduction at universities

Filho

Ribeiro

Setti

et al. 2023

Environ Dev Sustain

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“…Faezirad et al ( 78 ) introduced a novel model utilizing ML to combat food waste, particularly in university settings in Tehran, Iran. The reservation system, used to estimate food demand, sometimes led to unexplained meal waste.…”

Section: How Ai Relates To the Food Supplymentioning

confidence: 99%

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

Namkhah,

Fatemi,

Mansoori

et al. 2023

Front. Nutr.

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Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

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“…. , z k 􏼈 􏼉 is a matrix containing k centroid [32]. D(x i , z l ) is the distance from a sample point to a centroid.…”

Section: Principle Of the K-modes Algorithmmentioning

confidence: 99%

Research on the Innovative Marketing Model of E-Commerce Platform Driven by Big Data in the Era of Network Economy

Zhou

2022

Mobile Information Systems

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With the booming development of Internet information technology, e-commerce platforms in the era of network economy have undergone great changes, triggering a new marketing model change. Innovative research on marketing models can help the transformation and development of small and medium-sized e-commerce companies, which has important practical significance and theoretical value. The prediction of e-commerce sales is one of the key aspects of the evaluation of innovative marketing models, and only an accurate prediction of future sales can lead to a reasonable marketing plan. Therefore, a big data-driven e-commerce sales forecasting method is proposed. First of all, for 1703 real e-commerce companies, a large number of relevant data that affect sales are selected, including sales records, product information, product evaluation, and other information. A knowledge graph was then used to preprocess the data samples to produce a sample set containing concepts, entities, and relationships. Next, the knowledge graph K-modes clustering model is established. By fixing the affiliation matrix and the clustering cluster matrix in turn, the minimum of the objective function is continuously solved to obtain the cluster centres. Finally, sales prediction is achieved based on the clustering results. The experimental results show that the proposed clustering model is able to obtain better performance in terms of cluster purity, NMI, and F-value. The proposed clustering model has high sales prediction accuracy and has certain reference value for e-commerce enterprises of different scales to formulate innovative marketing models.

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Preventing food waste in subsidy-based university dining systems: An artificial neural network-aided model under uncertainty

Cited by 11 publications

References 36 publications

Toward food waste reduction at universities

Toward food waste reduction at universities

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications

Research on the Innovative Marketing Model of E-Commerce Platform Driven by Big Data in the Era of Network Economy

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