Reducing waste and achieving sustainable food security through optimizing surplus-food collection and meal distribution

Wu, Pei-Ju; Lin, Yi

doi:10.1007/s10479-023-05258-w

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…Since time immemorial, societies have been plagued by food waste and food insecurity (Akkerman et al, 2023;Orgut and Lodree, 2023;Reusken et al, 2023;Wu and Lin, 2023). Today, food banks play a vital role in addressing both these issues, but they remain highly reliant on human inspection of the quality of the food donations they receive, and on human decisions about how and where to store those that are deemed acceptable qualitatively.…”

Section: Managerial and Research Implications 51 Managerial Implicationsmentioning

confidence: 99%

Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics

Wu,

Tai

2024

JEIM

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PurposeIn the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.Design/methodology/approachFor diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.FindingsTesting of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.Research limitations/implicationsViewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.Practical implicationsAs well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.Originality/valueThis paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

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Section: Managerial and Research Implications 51 Managerial Implicationsmentioning

confidence: 99%

Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics

Wu,

Tai

2024

JEIM

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Endorsers measurement for decarbonised processed food supply chain through newly befitted interval valued neutrosophic vague sets

Sharma,

Tyagi,

Bhardwaj

2024

Ann Oper Res

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Comparing groups of units through composite indicators in a non-convex approach: corporate social responsibility for the food and beverage manufacturing industry

Kapelko,

Ortiz,

Aparicio

2024

Ann Oper Res

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This paper compares the performance of groups of units by composing indicators of corporate social responsibility (CSR) from an efficiency and productivity perspective, applicable across various industries. From a methodological perspective, our work extends the traditional input-oriented Benefit-of-the-Doubt (BoD) model in the multiplier form, by first adapting it to accommodate the non-convexities of the production set, and second, by innovatively applying it to compare indicators across groups of firms. This adaptation, pioneered in our study, leverages the framework previously established in the literature to address more complex scenarios. From an empirical perspective, we contribute by comparing the efficiency and productivity in CSR activities of food and beverage companies across regions of Europe, the United States and Canada, and Asia–Pacific over the period 2009–2018. The paper reveals that USA-Canadian firms tend to perform best with respect to CSR, followed by European firms, and that Asian-Pacific firms achieve the worst efficiency and productivity results. The study also shows that regional catching up in CSR productivity occurred over the analyzed period.

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Reducing waste and achieving sustainable food security through optimizing surplus-food collection and meal distribution

Cited by 4 publications

References 48 publications

Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics

Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics

Endorsers measurement for decarbonised processed food supply chain through newly befitted interval valued neutrosophic vague sets

Comparing groups of units through composite indicators in a non-convex approach: corporate social responsibility for the food and beverage manufacturing industry

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