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dc.contributor.authorChen, Tolyen_US
dc.contributor.authorChiu, Min-Chien_US
dc.date.accessioned2020-10-05T02:01:10Z-
dc.date.available2020-10-05T02:01:10Z-
dc.date.issued1970-01-01en_US
dc.identifier.issn2199-4536en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s40747-020-00179-8en_US
dc.identifier.urihttp://hdl.handle.net/11536/155211-
dc.description.abstractMost existing fuzzy collaborative forecasting (FCF) methods adopt type-1 fuzzy numbers to represent fuzzy forecasts. FCF methods based on interval-valued fuzzy numbers (IFNs) are not widely used. However, the inner and outer sections of an IFN-based fuzzy forecast provide meaning information that serves different managerial purposes, which is a desirable feature for a FCF method. This study proposed an IFN-based FCF approach. Unlike existing IFN-based fuzzy association rules or fuzzy inference systems, the IFN-based FCF approach ensures that all actual values fall within the corresponding fuzzy forecasts. In addition, the IFN-based FCF approach optimizes the forecasting precision and accuracy with the outer and inner sections of the aggregation result, respectively. Based on the experimental results, the proposed FCF-II approach surpassed existing methods in forecasting the yield of a dynamic random access memory product.en_US
dc.language.isoen_USen_US
dc.subjectFuzzy collaborative forecastingen_US
dc.subjectInterval fuzzy numberen_US
dc.subjectMixed binary nonlinear programmingen_US
dc.titleAn interval fuzzy number-based fuzzy collaborative forecasting approach for DRAM yield forecastingen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s40747-020-00179-8en_US
dc.identifier.journalCOMPLEX & INTELLIGENT SYSTEMSen_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000554441700002en_US
dc.citation.woscount0en_US
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