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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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>For 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Testing 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>Viewed 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>As 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This paper\u2019s 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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/jeim-10-2022-0398","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T07:02:29Z","timestamp":1706338949000},"page":"307-325","source":"Crossref","is-referenced-by-count":17,"title":["Artificial intelligence-based food-quality and warehousing management for food banks' inbound logistics"],"prefix":"10.1108","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6894-5391","authenticated-orcid":false,"given":"Pei-Ju","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6931-0144","authenticated-orcid":false,"given":"Yu-Chin","family":"Tai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"issue":"4","key":"key2024021910373763300_ref001","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1002\/bse.3198","article-title":"Sustainability in large food and beverage companies and their supply chains: an investigation into key drivers and barriers affecting sustainability strategies","volume":"32","year":"2023","journal-title":"Business Strategy and the Environment"},{"key":"key2024021910373763300_ref002","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2023.109215","article-title":"Optimal distribution of perishable foods with storage temperature control and quality requirements: an integrated vehicle routing problem","volume":"182","year":"2023","journal-title":"Computers & International Engineering"},{"issue":"3","key":"key2024021910373763300_ref003","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1287\/msom.2021.1018","article-title":"Shipment policies for products with fixed shelf lives: impact on profits and waste","volume":"24","year":"2022","journal-title":"M&som-Manufacturing & Service Operations Management"},{"key":"key2024021910373763300_ref004","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpe.2023.108926","article-title":"Dealing with donations: supply chain management challenges for food banks","volume":"262","year":"2023","journal-title":"International Journal of Production Management"},{"key":"key2024021910373763300_ref005","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1109\/EIT48999.2020.9208238","article-title":"An approach to optimize future inbound logistics processes using machine learning algorithms","year":"2020"},{"key":"key2024021910373763300_ref006","unstructured":"Albuquerque, L. 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