DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studiesen_US
dc.creatorWang, Hen_US
dc.creatorZhan, SLen_US
dc.creatorNg, CTen_US
dc.creatorCheng, TCEen_US
dc.date.accessioned2021-08-20T02:04:29Z-
dc.date.available2021-08-20T02:04:29Z-
dc.identifier.issn0925-5273en_US
dc.identifier.urihttp://hdl.handle.net/10397/90694-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectBayesian approachen_US
dc.subjectFood qualityen_US
dc.subjectGraphic evaluation and review techniqueen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectSustainabilityen_US
dc.titleCoordinating quality, time, and carbon emissions in perishable food production : a new technology integrating GERT and the Bayesian approachen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume225en_US
dc.identifier.doi10.1016/j.ijpe.2019.107570en_US
dcterms.abstractThis study concerns improving the performance of perishable food production from the joint perspective of management and technology. We consider a new idea about sustainable quality management for perishable food, which has aroused growing concern recently. Quality improvement activities (QIAs) should be carried out within the framework of the sustainable development. This motivates us to explore the tradeoffs among three sustainable metrics which involve quality, time and carbon emissions in perishable food production when optimizing QIA decision making. Our main contribution is proposing a new technology integrating Graphic Evaluation and Review Technique (GERT) and Bayesian approach, in which GERT can present the uncertainty of the three metrics and forecast their expected trends, and Bayesian approach can evaluate the probabilistic changes of the three metrics resulting from QIA decision making. To the best of our knowledge, this study is the first to use the above decision-making technology in food quality management. Furthermore, a multi-objective optimization model is built and a customized multi-objective particle swarm optimization is employed to generate the three-dimensional Pareto front to aid the decision making. We take bottled milk production as an example and present a case study on a famous Chinese dairy manufacturing firm. Numerical results and managerial insights show the advantages of our technology which include: (1) we can mitigate uncertainty, but do not change the random nature of food production; (2) we can reinforce the stability of the probabilistic change of the three metrics by increasing of the QIA-trial size; (3) we can visualize the optimal tradeoffs among the three metrics from different angles of view; and (4) we can figure out individualized sustainable quality management plans which are node-oriented and objective-oriented. In conclusion, we hope this study can be a beneficial supplement to the quality management field of perishable food with respect to technology innovation.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationInternational journal of production economics, July 2020, v. 225, 107570en_US
dcterms.isPartOfInternational journal of production economicsen_US
dcterms.issued2020-07-
dc.identifier.scopus2-s2.0-85076500765-
dc.identifier.artn107570en_US
dc.description.validate202108 bcvcen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1007-n04-
dc.identifier.SubFormID2417-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThis work is supported by the National Natural Science Foundation of China (Grant No. 71603237, 71433006, 91746202, 71874158, 71403245), and Zhejiang Provincial Natural Science Foundation of China (Grant No. LY19G030004, LY17G020003).en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023.07.31en_US
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